instruction stringlengths 314 345 | response stringlengths 7.2k 8.46k | query stringlengths 34 65 | num_papers int64 10 10 |
|---|---|---|---|
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the advantages of RNN over traditional approaches?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion",
"authors": "Muhan Na, Rui Liu, Feilong",
"year": 2022,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5780555555555555,
"url": "https://arxiv.org/abs/2209.11963v1"
},
{
"rank": 2,
"title": "What is the best RNN-cell structure to forecast each time series behavior?",
"authors": "Rohaifa Khaldi, Abdellatif El Afia, Raddouane Chiheb",
"year": 2022,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5558333333333333,
"url": "https://arxiv.org/abs/2203.07844v2"
},
{
"rank": 3,
"title": "Attention as an RNN",
"authors": "Leo Feng, Frederick Tung, Hossein Hajimirsadeghi",
"year": 2024,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5513888888888888,
"url": "https://arxiv.org/abs/2405.13956v2"
},
{
"rank": 4,
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"authors": "R. Bernabei, P. Belli, A. Bussolotti",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5402777777777777,
"url": "https://arxiv.org/abs/2504.17803v1"
},
{
"rank": 5,
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces of Eccentric Extrasolar Planets",
"authors": "Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5402777777777777,
"url": "https://arxiv.org/abs/2501.11667v1"
},
{
"rank": 6,
"title": "FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware",
"authors": "Korbinian Pรถppel, Maximilian Beck, Sepp Hochreiter",
"year": 2024,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5336111111111111,
"url": "https://arxiv.org/abs/2412.07752v3"
},
{
"rank": 7,
"title": "Gates Are Not What You Need in RNNs",
"authors": "Ronalds Zakovskis, Andis Draguns, Eliza Gaile",
"year": 2021,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5269444444444444,
"url": "https://arxiv.org/abs/2108.00527v3"
},
{
"rank": 8,
"title": "What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios and Locations",
"authors": "Chiara Plizzari, Toby Perrett, Barbara Caputo",
"year": 2023,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5136111111111111,
"url": "https://arxiv.org/abs/2306.08713v2"
},
{
"rank": 9,
"title": "The Galactic Chemical Evolution of phosphorus observed with IGRINS",
"authors": "G. Nandakumar, N. Ryde, M. Montelius",
"year": 2022,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5069444444444444,
"url": "https://arxiv.org/abs/2210.04940v1"
},
{
"rank": 10,
"title": "PRAXA: A Framework for What-If Analysis",
"authors": "Sneha Gathani, Kevin Li, Raghav Thind",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5047222222222222,
"url": "https://arxiv.org/abs/2510.09791v2"
}
],
"ranking_breakdown": [
{
"paper_id": "2209.11963",
"title": "A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional ",
"total_score": 0.5780555555555555,
"query_match": 0.38888888888888884,
"recency": 0.7,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2203.07844",
"title": "What is the best RNN-cell structure to forecast each time series behavior?",
"total_score": 0.5558333333333333,
"query_match": 0.3333333333333333,
"recency": 0.7,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2405.13956",
"title": "Attention as an RNN",
"total_score": 0.5513888888888888,
"query_match": 0.2222222222222222,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2504.17803",
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"total_score": 0.5402777777777777,
"query_match": 0.24444444444444444,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2501.11667",
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces",
"total_score": 0.5402777777777777,
"query_match": 0.24444444444444444,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2412.07752",
"title": "FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware",
"total_score": 0.5336111111111111,
"query_match": 0.2777777777777778,
"recency": 0.9,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2108.00527",
"title": "Gates Are Not What You Need in RNNs",
"total_score": 0.5269444444444444,
"query_match": 0.3111111111111111,
"recency": 0.6,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2306.08713",
"title": "What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios",
"total_score": 0.5136111111111111,
"query_match": 0.2777777777777778,
"recency": 0.8,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2210.04940",
"title": "The Galactic Chemical Evolution of phosphorus observed with IGRINS",
"total_score": 0.5069444444444444,
"query_match": 0.21111111111111108,
"recency": 0.7,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2510.09791",
"title": "PRAXA: A Framework for What-If Analysis",
"total_score": 0.5047222222222222,
"query_match": 0.15555555555555556,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the advantages of RNN over traditional approaches?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: arxiv(10)\n- ์ฐ๋ ๋ฒ์: 2021-2025\n- ํ๊ท ๋ญํน ์ ์: 0.535\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก arXiv, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: experimental(5), theoretical(5)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the advantages of RNN over traditional approaches? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the technical details of RNN?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Deep learning in surgical process modeling: A systematic review of workflow recognition.",
"authors": "Liu Zhenzhong, Chen Kelong, Wang Shuai",
"year": 2025,
"venue": "J Biomed Inform",
"study_type": "systematic_review",
"score": 0.6271428571428572,
"url": "https://pubmed.ncbi.nlm.nih.gov/39832608/"
},
{
"rank": 2,
"title": "A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images.",
"authors": "Sushith Mishmala, Sathiya A, Kalaipoonguzhali V",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.6042857142857143,
"url": "https://pubmed.ncbi.nlm.nih.gov/40307328/"
},
{
"rank": 3,
"title": "Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.",
"authors": "Yang Jiaqi, Du Bo, Zhang Liangpei",
"year": 2024,
"venue": "IEEE Trans Neural Netw Learn Syst",
"study_type": "experimental",
"score": 0.5978571428571429,
"url": "https://pubmed.ncbi.nlm.nih.gov/37279129/"
},
{
"rank": 4,
"title": "ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses.",
"authors": "Kim Jimin, Peng Minxian, Chen Shuqi",
"year": 2025,
"venue": "bioRxiv",
"study_type": "experimental",
"score": 0.5892857142857143,
"url": "https://pubmed.ncbi.nlm.nih.gov/40501917/"
},
{
"rank": 5,
"title": "Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications.",
"authors": "Rangaiah Pramod K B, Kumar B P Pradeep, Huss Fredrik",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5871428571428572,
"url": "https://pubmed.ncbi.nlm.nih.gov/40038450/"
},
{
"rank": 6,
"title": "Integration of RNN and CatBoost models in a tea-waste biochar filtration system for toxic organic pollutant removal efficiency prediction.",
"authors": "Jha Stuti, Gaur Rama, Shahabuddin Syed",
"year": 2025,
"venue": "RSC Adv",
"study_type": "theoretical",
"score": 0.5607142857142857,
"url": "https://pubmed.ncbi.nlm.nih.gov/40746784/"
},
{
"rank": 7,
"title": "A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process for SARS-CoV-2 spike protein production as a vaccine antigen.",
"authors": "Reyes Sebastian-Juan, Voyer Robert, Durocher Yves",
"year": 2025,
"venue": "Biotechnol Prog",
"study_type": "experimental",
"score": 0.5585714285714285,
"url": "https://pubmed.ncbi.nlm.nih.gov/40454837/"
},
{
"rank": 8,
"title": "What's behind tight deadlines? Business causes of technical debt",
"authors": "Rodrigo Rebouรงas de Almeida, Christoph Treude, Uirรก Kulesza",
"year": 2021,
"venue": "arXiv",
"study_type": "survey",
"score": 0.5539285714285714,
"url": "https://arxiv.org/abs/2104.09330v5"
},
{
"rank": 9,
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"authors": "R. Bernabei, P. Belli, A. Bussolotti",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5510714285714285,
"url": "https://arxiv.org/abs/2504.17803v1"
},
{
"rank": 10,
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces of Eccentric Extrasolar Planets",
"authors": "Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5510714285714285,
"url": "https://arxiv.org/abs/2501.11667v1"
}
],
"ranking_breakdown": [
{
"paper_id": "39832608",
"title": "Deep learning in surgical process modeling: A systematic review of workflow recognition.",
"total_score": 0.6271428571428572,
"query_match": 0.24285714285714283,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40307328",
"title": "A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus im",
"total_score": 0.6042857142857143,
"query_match": 0.2857142857142857,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "37279129",
"title": "Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.",
"total_score": 0.5978571428571429,
"query_match": 0.3571428571428571,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40501917",
"title": "ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiolog",
"total_score": 0.5892857142857143,
"query_match": 0.2857142857142857,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40038450",
"title": "Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications",
"total_score": 0.5871428571428572,
"query_match": 0.24285714285714283,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40746784",
"title": "Integration of RNN and CatBoost models in a tea-waste biochar filtration system for toxic organic po",
"total_score": 0.5607142857142857,
"query_match": 0.3142857142857143,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40454837",
"title": "A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process f",
"total_score": 0.5585714285714285,
"query_match": 0.1714285714285714,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2104.09330",
"title": "What's behind tight deadlines? Business causes of technical debt",
"total_score": 0.5539285714285714,
"query_match": 0.42857142857142855,
"recency": 0.6,
"study_type_priority": 0.6,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2504.17803",
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"total_score": 0.5510714285714285,
"query_match": 0.2714285714285714,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2501.11667",
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces",
"total_score": 0.5510714285714285,
"query_match": 0.2714285714285714,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the technical details of RNN?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(7), arxiv(3)\n- ์ฐ๋ ๋ฒ์: 2021-2025\n- ํ๊ท ๋ญํน ์ ์: 0.578\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก J Biomed Inform, Sci Rep, IEEE Trans Neural Netw Learn Syst์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(1), experimental(5), theoretical(3), survey(1)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the technical details of RNN? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: How has deep learning evolved over the past 5 years?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Review of applications of deep learning in veterinary diagnostics and animal health.",
"authors": "Xiao Sam, Dhand Navneet K, Wang Zhiyong",
"year": 2025,
"venue": "Front Vet Sci",
"study_type": "systematic_review",
"score": 0.651,
"url": "https://pubmed.ncbi.nlm.nih.gov/40144529/"
},
{
"rank": 2,
"title": "French guidelines for the diagnosis and management of Tourette syndrome.",
"authors": "Hartmann A, Ansquer S, Brefel-Courbon C",
"year": 2024,
"venue": "Rev Neurol (Paris)",
"study_type": "systematic_review",
"score": 0.6140000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/38760282/"
},
{
"rank": 3,
"title": "On the use of deep learning for computer-generated holography.",
"authors": "Yu Xuan, Zhang Haomiao, Zhao Zhe",
"year": 2025,
"venue": "iScience",
"study_type": "theoretical",
"score": 0.5910000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40491959/"
},
{
"rank": 4,
"title": "Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.",
"authors": "Lin Pei-Chun, Li Tsai-Chung, Huang Tzu-Hsuan",
"year": 2025,
"venue": "Front Public Health",
"study_type": "theoretical",
"score": 0.5740000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40756392/"
},
{
"rank": 5,
"title": "Decoding the Digital Pulse: Bibliometric Analysis of 25 Years in Digital Health Research Through the Journal of Medical Internet Research.",
"authors": "Kaczmarczyk Robert, Wilhelm Theresa Isabelle, Roos Jonas",
"year": 2024,
"venue": "J Med Internet Res",
"study_type": "theoretical",
"score": 0.554,
"url": "https://pubmed.ncbi.nlm.nih.gov/39546778/"
},
{
"rank": 6,
"title": "Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.",
"authors": "Shi Jin, Bendig David, Vollmar Horst Christian",
"year": 2023,
"venue": "J Med Internet Res",
"study_type": "theoretical",
"score": 0.538,
"url": "https://pubmed.ncbi.nlm.nih.gov/38064255/"
},
{
"rank": 7,
"title": "Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.",
"authors": "Gardezi Syed Jamal Safdar, Elazab Ahmed, Lei Baiying",
"year": 2019,
"venue": "J Med Internet Res",
"study_type": "systematic_review",
"score": 0.534,
"url": "https://pubmed.ncbi.nlm.nih.gov/31350843/"
},
{
"rank": 8,
"title": "Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images",
"authors": "Ajinkya Deshpande, Deep Gupta, Ankit Bhurane",
"year": 2024,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5305,
"url": "https://arxiv.org/abs/2412.03084v2"
},
{
"rank": 9,
"title": "PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis",
"authors": "Raghavendra Selvan, Bob Pepin, Christian Igel",
"year": 2024,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.49849999999999994,
"url": "https://arxiv.org/abs/2403.12562v2"
},
{
"rank": 10,
"title": "Inverse Contextual Bandits: Learning How Behavior Evolves over Time",
"authors": "Alihan Hรผyรผk, Daniel Jarrett, Mihaela van der Schaar",
"year": 2021,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.49449999999999994,
"url": "https://arxiv.org/abs/2107.06317v3"
}
],
"ranking_breakdown": [
{
"paper_id": "40144529",
"title": "Review of applications of deep learning in veterinary diagnostics and animal health.",
"total_score": 0.651,
"query_match": 0.33999999999999997,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "38760282",
"title": "French guidelines for the diagnosis and management of Tourette syndrome.",
"total_score": 0.6140000000000001,
"query_match": 0.26,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40491959",
"title": "On the use of deep learning for computer-generated holography.",
"total_score": 0.5910000000000001,
"query_match": 0.39,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40756392",
"title": "Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.",
"total_score": 0.5740000000000001,
"query_match": 0.31,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "39546778",
"title": "Decoding the Digital Pulse: Bibliometric Analysis of 25 Years in Digital Health Research Through the",
"total_score": 0.554,
"query_match": 0.31,
"recency": 0.9,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "38064255",
"title": "Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.",
"total_score": 0.538,
"query_match": 0.32,
"recency": 0.8,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "31350843",
"title": "Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.",
"total_score": 0.534,
"query_match": 0.31000000000000005,
"recency": 0.4,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2412.03084",
"title": "Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of ",
"total_score": 0.5305,
"query_match": 0.27,
"recency": 0.9,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2403.12562",
"title": "PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Imag",
"total_score": 0.49849999999999994,
"query_match": 0.19,
"recency": 0.9,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2107.06317",
"title": "Inverse Contextual Bandits: Learning How Behavior Evolves over Time",
"total_score": 0.49449999999999994,
"query_match": 0.32999999999999996,
"recency": 0.6,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: How has deep learning evolved over the past 5 years?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(7), arxiv(3)\n- ์ฐ๋ ๋ฒ์: 2019-2025\n- ํ๊ท ๋ญํน ์ ์: 0.558\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Front Vet Sci, Rev Neurol (Paris), iScience์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(3), theoretical(7)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | How has deep learning evolved over the past 5 years? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: Explain the architecture of ResNet
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.",
"authors": "Abbas Ghulam Husain, Sen Palash, Giri Oviya Anjali",
"year": 2025,
"venue": "Crit Care Explor",
"study_type": "meta_analysis",
"score": 0.638,
"url": "https://pubmed.ncbi.nlm.nih.gov/41348160/"
},
{
"rank": 2,
"title": "Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.",
"authors": "Velandia Hernando, Pardo Aldo, Vera Marรญa Isabel",
"year": 2025,
"venue": "Bioengineering (Basel)",
"study_type": "systematic_review",
"score": 0.6270000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41301204/"
},
{
"rank": 3,
"title": "Explainable Convolutional Neural Networks for the identification of the Ampullariidae genus.",
"authors": "Duwa Rabi Suraj, Suraj Kabir Salihu",
"year": 2026,
"venue": "Parasitol Int",
"study_type": "theoretical",
"score": 0.6220000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41271157/"
},
{
"rank": 4,
"title": "Towards Assessing Spread in Sets of Software Architecture Designs",
"authors": "Vittorio Cortellessa, J. Andres Diaz-Pace, Daniele Di Pompeo",
"year": 2024,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6145,
"url": "https://arxiv.org/abs/2402.19171v1"
},
{
"rank": 5,
"title": "Hybrid AI Pipeline for Laboratory Detection of Internal Potato Defects Using 2D RGB Imaging.",
"authors": "Hamdi Slim, Loukil Kais, Boubaker Adem Haj",
"year": 2025,
"venue": "J Imaging",
"study_type": "experimental",
"score": 0.611,
"url": "https://pubmed.ncbi.nlm.nih.gov/41440571/"
},
{
"rank": 6,
"title": "Quotient Network -- A Network Similar to ResNet but Learning Quotients",
"authors": "Peng Hui, Jiamuyang Zhao, Changxin Li",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5945,
"url": "https://arxiv.org/abs/2506.00992v1"
},
{
"rank": 7,
"title": "Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.",
"authors": "Afnaan K, Arunbalaji C G, Singh Tripty",
"year": 2025,
"venue": "Brain Inform",
"study_type": "experimental",
"score": 0.5870000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41348154/"
},
{
"rank": 8,
"title": "A comparative evaluation of explainability techniques for image data.",
"authors": "Skliarov Mykyta, Shawi Radwa El, Dhaoui Chedia",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5870000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41290895/"
},
{
"rank": 9,
"title": "Block-Level Genetic Algorithm Optimization for ResNet Endoscopic Image Classification.",
"authors": "De Souza Junior Gilberto R, De Sousa Filho Gilberto F, Cabral Lucidio Dos Anjos F",
"year": 2025,
"venue": "Annu Int Conf IEEE Eng Med Biol Soc",
"study_type": "theoretical",
"score": 0.5859999999999999,
"url": "https://pubmed.ncbi.nlm.nih.gov/41337417/"
},
{
"rank": 10,
"title": "Multi-phase deep learning framework with Multiscale Adaptive Swin Transformer and embedding attention for precision lung nodule detection and classification.",
"authors": "M Dhayalini, B Revathi Alias Ponmozhi",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5710000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41390502/"
}
],
"ranking_breakdown": [
{
"paper_id": "41348160",
"title": "Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Pati",
"total_score": 0.638,
"query_match": 0.22,
"recency": 1.0,
"study_type_priority": 1.0,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41301204",
"title": "Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diag",
"total_score": 0.6270000000000001,
"query_match": 0.28,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41271157",
"title": "Explainable Convolutional Neural Networks for the identification of the Ampullariidae genus.",
"total_score": 0.6220000000000001,
"query_match": 0.38,
"recency": 1.1,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2402.19171",
"title": "Towards Assessing Spread in Sets of Software Architecture Designs",
"total_score": 0.6145,
"query_match": 0.38,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41440571",
"title": "Hybrid AI Pipeline for Laboratory Detection of Internal Potato Defects Using 2D RGB Imaging.",
"total_score": 0.611,
"query_match": 0.33999999999999997,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2506.00992",
"title": "Quotient Network -- A Network Similar to ResNet but Learning Quotients",
"total_score": 0.5945,
"query_match": 0.28,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41348154",
"title": "Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.",
"total_score": 0.5870000000000001,
"query_match": 0.28,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41290895",
"title": "A comparative evaluation of explainability techniques for image data.",
"total_score": 0.5870000000000001,
"query_match": 0.28,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41337417",
"title": "Block-Level Genetic Algorithm Optimization for ResNet Endoscopic Image Classification.",
"total_score": 0.5859999999999999,
"query_match": 0.33999999999999997,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41390502",
"title": "Multi-phase deep learning framework with Multiscale Adaptive Swin Transformer and embedding attentio",
"total_score": 0.5710000000000001,
"query_match": 0.24,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: Explain the architecture of ResNet\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(8), arxiv(2)\n- ์ฐ๋ ๋ฒ์: 2024-2026\n- ํ๊ท ๋ญํน ์ ์: 0.604\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Crit Care Explor, Bioengineering (Basel), Parasitol Int์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: meta_analysis(1), systematic_review(1), theoretical(2), experimental(6)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | Explain the architecture of ResNet | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the technical details of RNN?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Deep learning in surgical process modeling: A systematic review of workflow recognition.",
"authors": "Liu Zhenzhong, Chen Kelong, Wang Shuai",
"year": 2025,
"venue": "J Biomed Inform",
"study_type": "systematic_review",
"score": 0.6271428571428572,
"url": "https://pubmed.ncbi.nlm.nih.gov/39832608/"
},
{
"rank": 2,
"title": "A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images.",
"authors": "Sushith Mishmala, Sathiya A, Kalaipoonguzhali V",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.6042857142857143,
"url": "https://pubmed.ncbi.nlm.nih.gov/40307328/"
},
{
"rank": 3,
"title": "Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.",
"authors": "Yang Jiaqi, Du Bo, Zhang Liangpei",
"year": 2024,
"venue": "IEEE Trans Neural Netw Learn Syst",
"study_type": "experimental",
"score": 0.5978571428571429,
"url": "https://pubmed.ncbi.nlm.nih.gov/37279129/"
},
{
"rank": 4,
"title": "ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses.",
"authors": "Kim Jimin, Peng Minxian, Chen Shuqi",
"year": 2025,
"venue": "bioRxiv",
"study_type": "experimental",
"score": 0.5892857142857143,
"url": "https://pubmed.ncbi.nlm.nih.gov/40501917/"
},
{
"rank": 5,
"title": "Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications.",
"authors": "Rangaiah Pramod K B, Kumar B P Pradeep, Huss Fredrik",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5871428571428572,
"url": "https://pubmed.ncbi.nlm.nih.gov/40038450/"
},
{
"rank": 6,
"title": "Integration of RNN and CatBoost models in a tea-waste biochar filtration system for toxic organic pollutant removal efficiency prediction.",
"authors": "Jha Stuti, Gaur Rama, Shahabuddin Syed",
"year": 2025,
"venue": "RSC Adv",
"study_type": "theoretical",
"score": 0.5607142857142857,
"url": "https://pubmed.ncbi.nlm.nih.gov/40746784/"
},
{
"rank": 7,
"title": "A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process for SARS-CoV-2 spike protein production as a vaccine antigen.",
"authors": "Reyes Sebastian-Juan, Voyer Robert, Durocher Yves",
"year": 2025,
"venue": "Biotechnol Prog",
"study_type": "experimental",
"score": 0.5585714285714285,
"url": "https://pubmed.ncbi.nlm.nih.gov/40454837/"
},
{
"rank": 8,
"title": "What's behind tight deadlines? Business causes of technical debt",
"authors": "Rodrigo Rebouรงas de Almeida, Christoph Treude, Uirรก Kulesza",
"year": 2021,
"venue": "arXiv",
"study_type": "survey",
"score": 0.5539285714285714,
"url": "https://arxiv.org/abs/2104.09330v5"
},
{
"rank": 9,
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"authors": "R. Bernabei, P. Belli, A. Bussolotti",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5510714285714285,
"url": "https://arxiv.org/abs/2504.17803v1"
},
{
"rank": 10,
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces of Eccentric Extrasolar Planets",
"authors": "Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5510714285714285,
"url": "https://arxiv.org/abs/2501.11667v1"
}
],
"ranking_breakdown": [
{
"paper_id": "39832608",
"title": "Deep learning in surgical process modeling: A systematic review of workflow recognition.",
"total_score": 0.6271428571428572,
"query_match": 0.24285714285714283,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40307328",
"title": "A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus im",
"total_score": 0.6042857142857143,
"query_match": 0.2857142857142857,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "37279129",
"title": "Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.",
"total_score": 0.5978571428571429,
"query_match": 0.3571428571428571,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40501917",
"title": "ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiolog",
"total_score": 0.5892857142857143,
"query_match": 0.2857142857142857,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40038450",
"title": "Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications",
"total_score": 0.5871428571428572,
"query_match": 0.24285714285714283,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40746784",
"title": "Integration of RNN and CatBoost models in a tea-waste biochar filtration system for toxic organic po",
"total_score": 0.5607142857142857,
"query_match": 0.3142857142857143,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40454837",
"title": "A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process f",
"total_score": 0.5585714285714285,
"query_match": 0.1714285714285714,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2104.09330",
"title": "What's behind tight deadlines? Business causes of technical debt",
"total_score": 0.5539285714285714,
"query_match": 0.42857142857142855,
"recency": 0.6,
"study_type_priority": 0.6,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2504.17803",
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"total_score": 0.5510714285714285,
"query_match": 0.2714285714285714,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2501.11667",
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces",
"total_score": 0.5510714285714285,
"query_match": 0.2714285714285714,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the technical details of RNN?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(7), arxiv(3)\n- ์ฐ๋ ๋ฒ์: 2021-2025\n- ํ๊ท ๋ญํน ์ ์: 0.578\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก J Biomed Inform, Sci Rep, IEEE Trans Neural Netw Learn Syst์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(1), experimental(5), theoretical(3), survey(1)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the technical details of RNN? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What do systematic reviews say about large language models?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Enhancing Systematic Reviews with Large Language Models: Using GPT-4 and Kimi",
"authors": "Dandan Chen Kaptur, Yue Huang, Xuejun Ryan Ji",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6852777777777779,
"url": "https://arxiv.org/abs/2504.20276v1"
},
{
"rank": 2,
"title": "Efficacy of Large Language Models in Systematic Reviews",
"authors": "Aaditya Shah, Shridhar Mehendale, Siddha Kanthi",
"year": 2024,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6669444444444445,
"url": "https://arxiv.org/abs/2408.04646v2"
},
{
"rank": 3,
"title": "A systematic review of research on large language models for computer programming education",
"authors": "Meina Zhu, Lanyu Xu, Barbara Ericson",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6647222222222222,
"url": "https://arxiv.org/abs/2506.21818v1"
},
{
"rank": 4,
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study",
"authors": "Lena Schmidt, Kaitlyn Hair, Sergio Graziosi",
"year": 2024,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6580555555555556,
"url": "https://arxiv.org/abs/2405.14445v2"
},
{
"rank": 5,
"title": "Advances in Pre-trained Language Models for Domain-Specific Text Classification: A Systematic Review",
"authors": "Zhyar Rzgar K. Rostam, Gรกbor Kertรฉsz",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6558333333333334,
"url": "https://arxiv.org/abs/2510.17892v1"
},
{
"rank": 6,
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"authors": "Yixuan Zhang, Joseph D Gaggiano, Nutchanon Yongsatianchot",
"year": 2023,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6247222222222223,
"url": "https://arxiv.org/abs/2302.03671v1"
},
{
"rank": 7,
"title": "Systematic Outliers in Large Language Models",
"authors": "Yongqi An, Xu Zhao, Tao Yu",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6247222222222222,
"url": "https://arxiv.org/abs/2502.06415v2"
},
{
"rank": 8,
"title": "Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers",
"authors": "Haoran Sun, Yusen Wu, Peng Wang",
"year": 2025,
"venue": "arXiv",
"study_type": "survey",
"score": 0.5913888888888889,
"url": "https://arxiv.org/abs/2502.09053v2"
},
{
"rank": 9,
"title": "Effectiveness of Chain-of-Thought in Distilling Reasoning Capability from Large Language Models",
"authors": "Cong-Thanh Do, Rama Doddipatla, Kate Knill",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5891666666666666,
"url": "https://arxiv.org/abs/2511.05184v1"
},
{
"rank": 10,
"title": "Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models",
"authors": "Ju-Young Kim, Ji-Hong Park, Se-Yeon Lee",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5891666666666666,
"url": "https://arxiv.org/abs/2512.08480v1"
}
],
"ranking_breakdown": [
{
"paper_id": "2504.20276",
"title": "Enhancing Systematic Reviews with Large Language Models: Using GPT-4 and Kimi",
"total_score": 0.6852777777777779,
"query_match": 0.4444444444444444,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.85,
"duplicate_penalty": 0.0
},
{
"paper_id": "2408.04646",
"title": "Efficacy of Large Language Models in Systematic Reviews",
"total_score": 0.6669444444444445,
"query_match": 0.4111111111111111,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2506.21818",
"title": "A systematic review of research on large language models for computer programming education",
"total_score": 0.6647222222222222,
"query_match": 0.3555555555555555,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2405.14445",
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasi",
"total_score": 0.6580555555555556,
"query_match": 0.38888888888888884,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2510.17892",
"title": "Advances in Pre-trained Language Models for Domain-Specific Text Classification: A Systematic Review",
"total_score": 0.6558333333333334,
"query_match": 0.3333333333333333,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2302.03671",
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"total_score": 0.6247222222222223,
"query_match": 0.3555555555555555,
"recency": 0.8,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2502.06415",
"title": "Systematic Outliers in Large Language Models",
"total_score": 0.6247222222222222,
"query_match": 0.3555555555555555,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2502.09053",
"title": "Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers",
"total_score": 0.5913888888888889,
"query_match": 0.3222222222222222,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2511.05184",
"title": "Effectiveness of Chain-of-Thought in Distilling Reasoning Capability from Large Language Models",
"total_score": 0.5891666666666666,
"query_match": 0.26666666666666666,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.08480",
"title": "Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detectio",
"total_score": 0.5891666666666666,
"query_match": 0.26666666666666666,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What do systematic reviews say about large language models?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: arxiv(10)\n- ์ฐ๋ ๋ฒ์: 2023-2025\n- ํ๊ท ๋ญํน ์ ์: 0.635\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก arXiv, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(6), experimental(3), survey(1)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What do systematic reviews say about large language models? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the technical details of RNN?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Deep learning in surgical process modeling: A systematic review of workflow recognition.",
"authors": "Liu Zhenzhong, Chen Kelong, Wang Shuai",
"year": 2025,
"venue": "J Biomed Inform",
"study_type": "systematic_review",
"score": 0.6271428571428572,
"url": "https://pubmed.ncbi.nlm.nih.gov/39832608/"
},
{
"rank": 2,
"title": "A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images.",
"authors": "Sushith Mishmala, Sathiya A, Kalaipoonguzhali V",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.6042857142857143,
"url": "https://pubmed.ncbi.nlm.nih.gov/40307328/"
},
{
"rank": 3,
"title": "Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.",
"authors": "Yang Jiaqi, Du Bo, Zhang Liangpei",
"year": 2024,
"venue": "IEEE Trans Neural Netw Learn Syst",
"study_type": "experimental",
"score": 0.5978571428571429,
"url": "https://pubmed.ncbi.nlm.nih.gov/37279129/"
},
{
"rank": 4,
"title": "ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses.",
"authors": "Kim Jimin, Peng Minxian, Chen Shuqi",
"year": 2025,
"venue": "bioRxiv",
"study_type": "experimental",
"score": 0.5892857142857143,
"url": "https://pubmed.ncbi.nlm.nih.gov/40501917/"
},
{
"rank": 5,
"title": "Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications.",
"authors": "Rangaiah Pramod K B, Kumar B P Pradeep, Huss Fredrik",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5871428571428572,
"url": "https://pubmed.ncbi.nlm.nih.gov/40038450/"
},
{
"rank": 6,
"title": "Integration of RNN and CatBoost models in a tea-waste biochar filtration system for toxic organic pollutant removal efficiency prediction.",
"authors": "Jha Stuti, Gaur Rama, Shahabuddin Syed",
"year": 2025,
"venue": "RSC Adv",
"study_type": "theoretical",
"score": 0.5607142857142857,
"url": "https://pubmed.ncbi.nlm.nih.gov/40746784/"
},
{
"rank": 7,
"title": "A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process for SARS-CoV-2 spike protein production as a vaccine antigen.",
"authors": "Reyes Sebastian-Juan, Voyer Robert, Durocher Yves",
"year": 2025,
"venue": "Biotechnol Prog",
"study_type": "experimental",
"score": 0.5585714285714285,
"url": "https://pubmed.ncbi.nlm.nih.gov/40454837/"
},
{
"rank": 8,
"title": "What's behind tight deadlines? Business causes of technical debt",
"authors": "Rodrigo Rebouรงas de Almeida, Christoph Treude, Uirรก Kulesza",
"year": 2021,
"venue": "arXiv",
"study_type": "survey",
"score": 0.5539285714285714,
"url": "https://arxiv.org/abs/2104.09330v5"
},
{
"rank": 9,
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"authors": "R. Bernabei, P. Belli, A. Bussolotti",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5510714285714285,
"url": "https://arxiv.org/abs/2504.17803v1"
},
{
"rank": 10,
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces of Eccentric Extrasolar Planets",
"authors": "Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5510714285714285,
"url": "https://arxiv.org/abs/2501.11667v1"
}
],
"ranking_breakdown": [
{
"paper_id": "39832608",
"title": "Deep learning in surgical process modeling: A systematic review of workflow recognition.",
"total_score": 0.6271428571428572,
"query_match": 0.24285714285714283,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40307328",
"title": "A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus im",
"total_score": 0.6042857142857143,
"query_match": 0.2857142857142857,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "37279129",
"title": "Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.",
"total_score": 0.5978571428571429,
"query_match": 0.3571428571428571,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40501917",
"title": "ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiolog",
"total_score": 0.5892857142857143,
"query_match": 0.2857142857142857,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40038450",
"title": "Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications",
"total_score": 0.5871428571428572,
"query_match": 0.24285714285714283,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40746784",
"title": "Integration of RNN and CatBoost models in a tea-waste biochar filtration system for toxic organic po",
"total_score": 0.5607142857142857,
"query_match": 0.3142857142857143,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40454837",
"title": "A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process f",
"total_score": 0.5585714285714285,
"query_match": 0.1714285714285714,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2104.09330",
"title": "What's behind tight deadlines? Business causes of technical debt",
"total_score": 0.5539285714285714,
"query_match": 0.42857142857142855,
"recency": 0.6,
"study_type_priority": 0.6,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2504.17803",
"title": "Proceedings to the 27th Workshop \"What Comes Beyond the Standard Models\" Bled, July 8-17, 2024",
"total_score": 0.5510714285714285,
"query_match": 0.2714285714285714,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2501.11667",
"title": "A One-Dimensional Energy Balance Model Parameterization for the Formation of CO2 Ice on the Surfaces",
"total_score": 0.5510714285714285,
"query_match": 0.2714285714285714,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the technical details of RNN?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(7), arxiv(3)\n- ์ฐ๋ ๋ฒ์: 2021-2025\n- ํ๊ท ๋ญํน ์ ์: 0.578\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก J Biomed Inform, Sci Rep, IEEE Trans Neural Netw Learn Syst์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(1), experimental(5), theoretical(3), survey(1)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the technical details of RNN? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the recent trends in biomedical?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Artificial Intelligence Tools in Biomedical Research: Part 1-Literature Search and Knowledge Mining.",
"authors": "Sen Chandan K",
"year": 2025,
"venue": "Antioxid Redox Signal",
"study_type": "systematic_review",
"score": 0.6578571428571429,
"url": "https://pubmed.ncbi.nlm.nih.gov/41433042/"
},
{
"rank": 2,
"title": "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature",
"authors": "Jonathan Roth, Pedro H. C. Sant'Anna, Alyssa Bilinski",
"year": 2022,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6224999999999999,
"url": "https://arxiv.org/abs/2201.01194v3"
},
{
"rank": 3,
"title": "A Survey on Current Trends and Recent Advances in Text Anonymization",
"authors": "Tobias Deuรer, Lorenz Sparrenberg, Armin Berger",
"year": 2025,
"venue": "arXiv",
"study_type": "survey",
"score": 0.6167857142857143,
"url": "https://arxiv.org/abs/2508.21587v1"
},
{
"rank": 4,
"title": "The National Poison data System<sup>ยฎ</sup>: a bibliographic review of publication trends and topic modeling using language models.",
"authors": "Weigel Bernard, McCrary Quincy, Zweerink Kara",
"year": 2025,
"venue": "Clin Toxicol (Phila)",
"study_type": "theoretical",
"score": 0.6099999999999999,
"url": "https://pubmed.ncbi.nlm.nih.gov/41432768/"
},
{
"rank": 5,
"title": "Challenges of Trustworthy Federated Learning: What's Done, Current Trends and Remaining Work",
"authors": "Nuria Rodrรญguez-Barroso, Mario Garcรญa-Mรกrquez, M. Victoria Luzรณn",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.6024999999999999,
"url": "https://arxiv.org/abs/2507.15796v1"
},
{
"rank": 6,
"title": "Systematic Review: Proteomics-Driven Multi-Omics Integration for Alzheimer's Disease Pathology and Precision Medicine.",
"authors": "Dong Jonathan Mingsong, Zhong Huan",
"year": 2025,
"venue": "Neurol Int",
"study_type": "systematic_review",
"score": 0.6007142857142858,
"url": "https://pubmed.ncbi.nlm.nih.gov/41441216/"
},
{
"rank": 7,
"title": "Five decades of total ankle replacement: from early failures to fourth-generation innovations and future priorities.",
"authors": "Colรฒ Gabriele, Fusini Federico, Mazzotti Antonio",
"year": 2025,
"venue": "Eur J Orthop Surg Traumatol",
"study_type": "systematic_review",
"score": 0.5985714285714285,
"url": "https://pubmed.ncbi.nlm.nih.gov/41460300/"
},
{
"rank": 8,
"title": "What drives Malaysian medical students' specialty choices? Findings from the MMSCI study.",
"authors": "Ser Gerald Tze Zhen, Teng Zhi Sean, Hong Wei-Han",
"year": 2025,
"venue": "Hum Resour Health",
"study_type": "survey",
"score": 0.5978571428571429,
"url": "https://pubmed.ncbi.nlm.nih.gov/41454333/"
},
{
"rank": 9,
"title": "The research trends of macrophages in acute kidney injury: A bibliometric analysis.",
"authors": "Zhang Xiangyu, Zhou Qiao, Bao Sizeng",
"year": 2025,
"venue": "Medicine (Baltimore)",
"study_type": "other",
"score": 0.5957142857142856,
"url": "https://pubmed.ncbi.nlm.nih.gov/41465886/"
},
{
"rank": 10,
"title": "Past, Present, and Future of Sodium Hypochlorite in Dermatology: A Scoping Review.",
"authors": "Chang Christy H, Devine Maura, Woo Deborah",
"year": 2025,
"venue": "Am J Clin Dermatol",
"study_type": "systematic_review",
"score": 0.5950000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41428161/"
}
],
"ranking_breakdown": [
{
"paper_id": "41433042",
"title": "Artificial Intelligence Tools in Biomedical Research: Part 1-Literature Search and Knowledge Mining.",
"total_score": 0.6578571428571429,
"query_match": 0.3571428571428571,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2201.01194",
"title": "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature",
"total_score": 0.6224999999999999,
"query_match": 0.5,
"recency": 0.7,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2508.21587",
"title": "A Survey on Current Trends and Recent Advances in Text Anonymization",
"total_score": 0.6167857142857143,
"query_match": 0.3857142857142857,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41432768",
"title": "The National Poison data System<sup>ยฎ</sup>: a bibliographic review of publication trends and topic ",
"total_score": 0.6099999999999999,
"query_match": 0.39999999999999997,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2507.15796",
"title": "Challenges of Trustworthy Federated Learning: What's Done, Current Trends and Remaining Work",
"total_score": 0.6024999999999999,
"query_match": 0.39999999999999997,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41441216",
"title": "Systematic Review: Proteomics-Driven Multi-Omics Integration for Alzheimer's Disease Pathology and P",
"total_score": 0.6007142857142858,
"query_match": 0.21428571428571427,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41460300",
"title": "Five decades of total ankle replacement: from early failures to fourth-generation innovations and fu",
"total_score": 0.5985714285714285,
"query_match": 0.1714285714285714,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41454333",
"title": "What drives Malaysian medical students' specialty choices? Findings from the MMSCI study.",
"total_score": 0.5978571428571429,
"query_match": 0.3571428571428571,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41465886",
"title": "The research trends of macrophages in acute kidney injury: A bibliometric analysis.",
"total_score": 0.5957142857142856,
"query_match": 0.41428571428571426,
"recency": 1.0,
"study_type_priority": 0.4,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41428161",
"title": "Past, Present, and Future of Sodium Hypochlorite in Dermatology: A Scoping Review.",
"total_score": 0.5950000000000001,
"query_match": 0.19999999999999998,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the recent trends in biomedical?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(7), arxiv(3)\n- ์ฐ๋ ๋ฒ์: 2022-2025\n- ํ๊ท ๋ญํน ์ ์: 0.610\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Antioxid Redox Signal, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(4), experimental(1), survey(2), theoretical(2), other(1)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the recent trends in biomedical? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: How has transformer evolved over the past 5 years?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Decoding the Digital Pulse: Bibliometric Analysis of 25 Years in Digital Health Research Through the Journal of Medical Internet Research.",
"authors": "Kaczmarczyk Robert, Wilhelm Theresa Isabelle, Roos Jonas",
"year": 2024,
"venue": "J Med Internet Res",
"study_type": "theoretical",
"score": 0.5544444444444445,
"url": "https://pubmed.ncbi.nlm.nih.gov/39546778/"
},
{
"rank": 2,
"title": "Full Stack Optimization of Transformer Inference: a Survey",
"authors": "Sehoon Kim, Coleman Hooper, Thanakul Wattanawong",
"year": 2023,
"venue": "arXiv",
"study_type": "case_study",
"score": 0.5380555555555555,
"url": "https://arxiv.org/abs/2302.14017v1"
},
{
"rank": 3,
"title": "Evolutionary Trends in Decision Sciences Education Research from Simulation and Games to Big Data Analytics and Generative Artificial Intelligence.",
"authors": "Akpan Ikpe Justice, Razavi Rouzbeh, Akpan Asuama A",
"year": 2025,
"venue": "Big Data",
"study_type": "theoretical",
"score": 0.5166666666666666,
"url": "https://pubmed.ncbi.nlm.nih.gov/40019821/"
},
{
"rank": 4,
"title": "MLP Can Be A Good Transformer Learner",
"authors": "Sihao Lin, Pumeng Lyu, Dongrui Liu",
"year": 2024,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5113888888888889,
"url": "https://arxiv.org/abs/2404.05657v1"
},
{
"rank": 5,
"title": "SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance",
"authors": "Jarrod Haas",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.49583333333333335,
"url": "https://arxiv.org/abs/2308.10973v1"
},
{
"rank": 6,
"title": "The Evolved Transformer",
"authors": "David R. So, Chen Liang, Quoc V. Le",
"year": 2019,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.49583333333333335,
"url": "https://arxiv.org/abs/1901.11117v4"
},
{
"rank": 7,
"title": "The gauge theory dual of the bilayer XY model with second order Josephson coupling",
"authors": "Pye Ton How, Sungkit Yip",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.4913888888888889,
"url": "https://arxiv.org/abs/2507.19401v2"
},
{
"rank": 8,
"title": "PIGEON: Predicting Image Geolocations",
"authors": "Lukas Haas, Michal Skreta, Silas Alberti",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.48250000000000004,
"url": "https://arxiv.org/abs/2307.05845v6"
},
{
"rank": 9,
"title": "Glance-and-Gaze Vision Transformer",
"authors": "Qihang Yu, Yingda Xia, Yutong Bai",
"year": 2021,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.47805555555555546,
"url": "https://arxiv.org/abs/2106.02277v1"
},
{
"rank": 10,
"title": "Big Data Science Over the Past Web",
"authors": "Miguel Costa, Julien Masanรจs",
"year": 2021,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.4691666666666666,
"url": "https://arxiv.org/abs/2108.01605v1"
}
],
"ranking_breakdown": [
{
"paper_id": "39546778",
"title": "Decoding the Digital Pulse: Bibliometric Analysis of 25 Years in Digital Health Research Through the",
"total_score": 0.5544444444444445,
"query_match": 0.3111111111111111,
"recency": 0.9,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2302.14017",
"title": "Full Stack Optimization of Transformer Inference: a Survey",
"total_score": 0.5380555555555555,
"query_match": 0.28888888888888886,
"recency": 0.8,
"study_type_priority": 0.6,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "40019821",
"title": "Evolutionary Trends in Decision Sciences Education Research from Simulation and Games to Big Data An",
"total_score": 0.5166666666666666,
"query_match": 0.16666666666666666,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2404.05657",
"title": "MLP Can Be A Good Transformer Learner",
"total_score": 0.5113888888888889,
"query_match": 0.12222222222222222,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2308.10973",
"title": "SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euc",
"total_score": 0.49583333333333335,
"query_match": 0.13333333333333333,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "1901.11117",
"title": "The Evolved Transformer",
"total_score": 0.49583333333333335,
"query_match": 0.3333333333333333,
"recency": 0.4,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2507.19401",
"title": "The gauge theory dual of the bilayer XY model with second order Josephson coupling",
"total_score": 0.4913888888888889,
"query_match": 0.12222222222222222,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2307.05845",
"title": "PIGEON: Predicting Image Geolocations",
"total_score": 0.48250000000000004,
"query_match": 0.09999999999999999,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2106.02277",
"title": "Glance-and-Gaze Vision Transformer",
"total_score": 0.47805555555555546,
"query_match": 0.18888888888888888,
"recency": 0.6,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2108.01605",
"title": "Big Data Science Over the Past Web",
"total_score": 0.4691666666666666,
"query_match": 0.26666666666666666,
"recency": 0.6,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: How has transformer evolved over the past 5 years?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(2), arxiv(8)\n- ์ฐ๋ ๋ฒ์: 2019-2025\n- ํ๊ท ๋ญํน ์ ์: 0.503\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก J Med Internet Res, arXiv, Big Data์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: theoretical(4), case_study(1), experimental(5)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | How has transformer evolved over the past 5 years? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the advantages of attention over traditional approaches?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Rethinking Hip Surgery: A Systematic Review of Sparing Piriformis and Internus, Repairing Externus (SPAIRE) vs. Traditional Hemiarthroplasty Approaches.",
"authors": "Raufi Muhammad Y, Shrestha Sagaurav, Hamsho Ward",
"year": 2025,
"venue": "Cureus",
"study_type": "systematic_review",
"score": 0.6883333333333334,
"url": "https://pubmed.ncbi.nlm.nih.gov/40896070/"
},
{
"rank": 2,
"title": "CCFormer: Cross-Modal Cross-Attention Transformer for Classification of Hyperspectral and LiDAR Data.",
"authors": "Guo Hufeng, Tian Baohui, Liu Wenyi",
"year": 2025,
"venue": "Sensors (Basel)",
"study_type": "experimental",
"score": 0.6127777777777778,
"url": "https://pubmed.ncbi.nlm.nih.gov/41012936/"
},
{
"rank": 3,
"title": "Diverse behavior clustering of students on campus with macroscopic attention.",
"authors": "Chen Wanghu, Wu Zongjuan, Zeng Siqi",
"year": 2025,
"venue": "Sci Rep",
"study_type": "case_study",
"score": 0.5927777777777777,
"url": "https://pubmed.ncbi.nlm.nih.gov/40813806/"
},
{
"rank": 4,
"title": "Pedestrian detection in aerial image based on convolutional neural network with attention mechanism and multi-scale prediction.",
"authors": "Yang Jiaxi, Shen Jiaquan, Wang Shitong",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5772222222222222,
"url": "https://pubmed.ncbi.nlm.nih.gov/41387459/"
},
{
"rank": 5,
"title": "The advantages of TCM in the treatment of gynecologic malignancies.",
"authors": "Jiang Wei, Yuan Jinghua, Zhang Xinling",
"year": 2025,
"venue": "Front Oncol",
"study_type": "other",
"score": 0.5750000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40978061/"
},
{
"rank": 6,
"title": "Advancing oral squamous cell carcinoma research: the evolving role of patient-derived cell lines, xenografts, and organoid models.",
"authors": "Upadhyay Vinal, Das Sourav, Nehru Amisha",
"year": 2025,
"venue": "3 Biotech",
"study_type": "theoretical",
"score": 0.5727777777777778,
"url": "https://pubmed.ncbi.nlm.nih.gov/40822425/"
},
{
"rank": 7,
"title": "A modular deep learning pipeline for enhanced plane-wave beamforming and B-mode image quality.",
"authors": "Hadri Hamza, Fail Abderahhim, Sadik Mohamed",
"year": 2025,
"venue": "Med Phys",
"study_type": "experimental",
"score": 0.5700000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40698752/"
},
{
"rank": 8,
"title": "Attention as a Hypernetwork",
"authors": "Simon Schug, Seijin Kobayashi, Yassir Akram",
"year": 2024,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5647222222222222,
"url": "https://arxiv.org/abs/2406.05816v4"
},
{
"rank": 9,
"title": "Large-Scale Production and Therapeutic Evaluation of Exosomes for Cancer Treatment.",
"authors": "Kimiz-Gebologlu Ilgin, Oncel Suphi S",
"year": 2025,
"venue": "Thorac Res Pract",
"study_type": "theoretical",
"score": 0.5638888888888889,
"url": "https://pubmed.ncbi.nlm.nih.gov/41340272/"
},
{
"rank": 10,
"title": "Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability.",
"authors": "Hammam Islam M, El-Kharbotly Amin K, Sadek Yomna M",
"year": 2025,
"venue": "Sci Rep",
"study_type": "theoretical",
"score": 0.5638888888888889,
"url": "https://pubmed.ncbi.nlm.nih.gov/41188434/"
}
],
"ranking_breakdown": [
{
"paper_id": "40896070",
"title": "Rethinking Hip Surgery: A Systematic Review of Sparing Piriformis and Internus, Repairing Externus (",
"total_score": 0.6883333333333334,
"query_match": 0.43333333333333335,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41012936",
"title": "CCFormer: Cross-Modal Cross-Attention Transformer for Classification of Hyperspectral and LiDAR Data",
"total_score": 0.6127777777777778,
"query_match": 0.34444444444444444,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40813806",
"title": "Diverse behavior clustering of students on campus with macroscopic attention.",
"total_score": 0.5927777777777777,
"query_match": 0.34444444444444444,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41387459",
"title": "Pedestrian detection in aerial image based on convolutional neural network with attention mechanism ",
"total_score": 0.5772222222222222,
"query_match": 0.25555555555555554,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40978061",
"title": "The advantages of TCM in the treatment of gynecologic malignancies.",
"total_score": 0.5750000000000001,
"query_match": 0.4,
"recency": 1.0,
"study_type_priority": 0.4,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40822425",
"title": "Advancing oral squamous cell carcinoma research: the evolving role of patient-derived cell lines, xe",
"total_score": 0.5727777777777778,
"query_match": 0.34444444444444444,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40698752",
"title": "A modular deep learning pipeline for enhanced plane-wave beamforming and B-mode image quality.",
"total_score": 0.5700000000000001,
"query_match": 0.19999999999999998,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2406.05816",
"title": "Attention as a Hypernetwork",
"total_score": 0.5647222222222222,
"query_match": 0.25555555555555554,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41340272",
"title": "Large-Scale Production and Therapeutic Evaluation of Exosomes for Cancer Treatment.",
"total_score": 0.5638888888888889,
"query_match": 0.3222222222222222,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41188434",
"title": "Adaptive demand forecasting framework with weighted ensemble of regression and machine learning mode",
"total_score": 0.5638888888888889,
"query_match": 0.3222222222222222,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the advantages of attention over traditional approaches?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(9), arxiv(1)\n- ์ฐ๋ ๋ฒ์: 2024-2025\n- ํ๊ท ๋ญํน ์ ์: 0.588\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Cureus, Sensors (Basel), Sci Rep์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(1), experimental(4), case_study(1), other(1), theoretical(3)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the advantages of attention over traditional approaches? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: Compare self-supervised vs semi-supervised for classification
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering",
"authors": "Songbo Wang, Renchi Yang, Yurui Lai",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6825,
"url": "https://arxiv.org/abs/2511.19976v1"
},
{
"rank": 2,
"title": "Systematic comparison of semi-supervised and self-supervised learning for medical image classification",
"authors": "Zhe Huang, Ruijie Jiang, Shuchin Aeron",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6710714285714285,
"url": "https://arxiv.org/abs/2307.08919v3"
},
{
"rank": 3,
"title": "ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data",
"authors": "Melvin Barbaux",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6653571428571428,
"url": "https://arxiv.org/abs/2512.13228v1"
},
{
"rank": 4,
"title": "Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis",
"authors": "Linhao Qu, Siyu Liu, Xiaoyu Liu",
"year": 2022,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6167857142857143,
"url": "https://arxiv.org/abs/2208.08789v2"
},
{
"rank": 5,
"title": "Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification",
"authors": "Pranav Singh, Raviteja Chukkapalli, Shravan Chaudhari",
"year": 2023,
"venue": "arXiv",
"study_type": "other",
"score": 0.6110714285714286,
"url": "https://arxiv.org/abs/2311.10319v6"
},
{
"rank": 6,
"title": "FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning",
"authors": "Yidong Wang, Hao Chen, Qiang Heng",
"year": 2022,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6053571428571428,
"url": "https://arxiv.org/abs/2205.07246v3"
},
{
"rank": 7,
"title": "Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks",
"authors": "Fangyijie Wang, Kathleen M. Curran, Guรฉnolรฉ Silvestre",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5967857142857143,
"url": "https://arxiv.org/abs/2503.17057v1"
},
{
"rank": 8,
"title": "Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification",
"authors": "Fengbei Liu, Yu Tian, Filipe R. Cordeiro",
"year": 2021,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5910714285714285,
"url": "https://arxiv.org/abs/2103.03629v3"
},
{
"rank": 9,
"title": "InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning",
"authors": "Zhe Huang, Xiaowei Yu, Dajiang Zhu",
"year": 2024,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5882142857142857,
"url": "https://arxiv.org/abs/2403.10658v1"
},
{
"rank": 10,
"title": "SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning",
"authors": "Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon",
"year": 2021,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5853571428571428,
"url": "https://arxiv.org/abs/2101.06480v1"
}
],
"ranking_breakdown": [
{
"paper_id": "2511.19976",
"title": "Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering",
"total_score": 0.6825,
"query_match": 0.5,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2307.08919",
"title": "Systematic comparison of semi-supervised and self-supervised learning for medical image classificati",
"total_score": 0.6710714285714285,
"query_match": 0.5714285714285714,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.13228",
"title": "ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data",
"total_score": 0.6653571428571428,
"query_match": 0.4571428571428571,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2208.08789",
"title": "Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Le",
"total_score": 0.6167857142857143,
"query_match": 0.3857142857142857,
"recency": 0.7,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2311.10319",
"title": "Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automati",
"total_score": 0.6110714285714286,
"query_match": 0.5714285714285714,
"recency": 0.8,
"study_type_priority": 0.4,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2205.07246",
"title": "FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning",
"total_score": 0.6053571428571428,
"query_match": 0.4571428571428571,
"recency": 0.7,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2503.17057",
"title": "Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks",
"total_score": 0.5967857142857143,
"query_match": 0.3857142857142857,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2103.03629",
"title": "Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification",
"total_score": 0.5910714285714285,
"query_match": 0.5714285714285714,
"recency": 0.6,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2403.10658",
"title": "InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning",
"total_score": 0.5882142857142857,
"query_match": 0.3142857142857143,
"recency": 0.9,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2101.06480",
"title": "SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning",
"total_score": 0.5853571428571428,
"query_match": 0.4571428571428571,
"recency": 0.6,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: Compare self-supervised vs semi-supervised for classification\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: arxiv(10)\n- ์ฐ๋ ๋ฒ์: 2021-2025\n- ํ๊ท ๋ญํน ์ ์: 0.621\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก arXiv, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: experimental(6), systematic_review(1), other(1), theoretical(2)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | Compare self-supervised vs semi-supervised for classification | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: How does attention work in large language models?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Fine-tuning large language models in federated learning with fairness-aware prompt selection.",
"authors": "Jiang Yalan, Li Zhongliang, Song Bin",
"year": 2026,
"venue": "Neural Netw",
"study_type": "experimental",
"score": 0.7150000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41072284/"
},
{
"rank": 2,
"title": "A systematic review of ethical considerations of large language models in healthcare and medicine.",
"authors": "Fareed Muhammad, Fatima Madeeha, Uddin Jamal",
"year": 2025,
"venue": "Front Digit Health",
"study_type": "systematic_review",
"score": 0.7050000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/41019285/"
},
{
"rank": 3,
"title": "The potential existential threat of large language models to online survey research.",
"authors": "Westwood Sean J",
"year": 2025,
"venue": "Proc Natl Acad Sci U S A",
"study_type": "survey",
"score": 0.665,
"url": "https://pubmed.ncbi.nlm.nih.gov/41264250/"
},
{
"rank": 4,
"title": "Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models",
"authors": "Ju-Young Kim, Ji-Hong Park, Se-Yeon Lee",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6575,
"url": "https://arxiv.org/abs/2512.08480v1"
},
{
"rank": 5,
"title": "Preprocessing narrative texts in electronic medical records to identify hospital adverse events: A scoping review.",
"authors": "Jafarpour Hamed, Wu Guosong, Cheligeer Cheligeer Ken",
"year": 2025,
"venue": "Artif Intell Med",
"study_type": "systematic_review",
"score": 0.655,
"url": "https://pubmed.ncbi.nlm.nih.gov/41072367/"
},
{
"rank": 6,
"title": "Large language models in radiology workflows: An exploratory study of generative AI for non-visual tasks in the German healthcare system.",
"authors": "Steinhauser Stefanie, Welsch Sabrina",
"year": 2025,
"venue": "Health Policy",
"study_type": "theoretical",
"score": 0.655,
"url": "https://pubmed.ncbi.nlm.nih.gov/40972429/"
},
{
"rank": 7,
"title": "A Survey of AIOps in the Era of Large Language Models",
"authors": "Lingzhe Zhang, Tong Jia, Mengxi Jia",
"year": 2025,
"venue": "arXiv",
"study_type": "survey",
"score": 0.6525,
"url": "https://arxiv.org/abs/2507.12472v1"
},
{
"rank": 8,
"title": "Feasibility evaluation of large language models in anesthesia-specific post-operative care instructions for total knee arthroplasty.",
"authors": "Nagesh Dhruv, Keating Donald P, Divakaruni Raghu V",
"year": 2026,
"venue": "PEC Innov",
"study_type": "theoretical",
"score": 0.645,
"url": "https://pubmed.ncbi.nlm.nih.gov/41403686/"
},
{
"rank": 9,
"title": "Vaner2: towards more general biomedical named entity recognition using multi-task large language model encoders.",
"authors": "Liu Yuxuan, Bian Junyi, Zhai Weiqi",
"year": 2026,
"venue": "Health Inf Sci Syst",
"study_type": "experimental",
"score": 0.635,
"url": "https://pubmed.ncbi.nlm.nih.gov/41281609/"
},
{
"rank": 10,
"title": "Unforgettable Generalization in Language Models",
"authors": "Eric Zhang, Leshem Chosen, Jacob Andreas",
"year": 2024,
"venue": "arXiv",
"study_type": "randomized_trial",
"score": 0.6325000000000001,
"url": "https://arxiv.org/abs/2409.02228v1"
}
],
"ranking_breakdown": [
{
"paper_id": "41072284",
"title": "Fine-tuning large language models in federated learning with fairness-aware prompt selection.",
"total_score": 0.7150000000000001,
"query_match": 0.5125,
"recency": 1.1,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41019285",
"title": "A systematic review of ethical considerations of large language models in healthcare and medicine.",
"total_score": 0.7050000000000001,
"query_match": 0.475,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41264250",
"title": "The potential existential threat of large language models to online survey research.",
"total_score": 0.665,
"query_match": 0.4875,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.08480",
"title": "Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detectio",
"total_score": 0.6575,
"query_match": 0.4375,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41072367",
"title": "Preprocessing narrative texts in electronic medical records to identify hospital adverse events: A s",
"total_score": 0.655,
"query_match": 0.3125,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40972429",
"title": "Large language models in radiology workflows: An exploratory study of generative AI for non-visual t",
"total_score": 0.655,
"query_match": 0.5125,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2507.12472",
"title": "A Survey of AIOps in the Era of Large Language Models",
"total_score": 0.6525,
"query_match": 0.475,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41403686",
"title": "Feasibility evaluation of large language models in anesthesia-specific post-operative care instructi",
"total_score": 0.645,
"query_match": 0.475,
"recency": 1.1,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41281609",
"title": "Vaner2: towards more general biomedical named entity recognition using multi-task large language mod",
"total_score": 0.635,
"query_match": 0.35,
"recency": 1.1,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2409.02228",
"title": "Unforgettable Generalization in Language Models",
"total_score": 0.6325000000000001,
"query_match": 0.375,
"recency": 0.9,
"study_type_priority": 0.8,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: How does attention work in large language models?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(7), arxiv(3)\n- ์ฐ๋ ๋ฒ์: 2024-2026\n- ํ๊ท ๋ญํน ์ ์: 0.662\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Neural Netw, Front Digit Health, Proc Natl Acad Sci U S A์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: experimental(3), systematic_review(2), survey(2), theoretical(2), randomized_trial(1)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | How does attention work in large language models? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What do systematic reviews say about attention mechanism?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"authors": "Yixuan Zhang, Joseph D Gaggiano, Nutchanon Yongsatianchot",
"year": 2023,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6425000000000001,
"url": "https://arxiv.org/abs/2302.03671v1"
},
{
"rank": 2,
"title": "Trust and Trustworthiness from Human-Centered Perspective in HRI -- A Systematic Literature Review",
"authors": "Debora Firmino de Souza, Sonia Sousa, Kadri Kristjuhan-Ling",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6075,
"url": "https://arxiv.org/abs/2501.19323v1"
},
{
"rank": 3,
"title": "Computational-Assisted Systematic Review and Meta-Analysis (CASMA): Effect of a Subclass of GnRH-a on Endometriosis Recurrence",
"authors": "Sandro Tsang",
"year": 2025,
"venue": "arXiv",
"study_type": "meta_analysis",
"score": 0.5975,
"url": "https://arxiv.org/abs/2509.16599v3"
},
{
"rank": 4,
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study",
"authors": "Lena Schmidt, Kaitlyn Hair, Sergio Graziosi",
"year": 2024,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5825,
"url": "https://arxiv.org/abs/2405.14445v2"
},
{
"rank": 5,
"title": "Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review",
"authors": "Nicola Rossberg, Celina L. Li, Simone Innocente",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5625,
"url": "https://arxiv.org/abs/2503.02905v1"
},
{
"rank": 6,
"title": "Non-invasive Techniques for Flow Rate Measurement in Water Pipes: Protocol for a Systematic Review",
"authors": "Juan Diego Belesaca, Fabian Astudillo Salinas",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5625,
"url": "https://arxiv.org/abs/2512.09239v1"
},
{
"rank": 7,
"title": "Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review",
"authors": "Katherine Wang, Simon Julier, Youngjun Cho",
"year": 2022,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5425,
"url": "https://arxiv.org/abs/2204.00719v1"
},
{
"rank": 8,
"title": "Pay Attention to What You Need",
"authors": "Yifei Gao, Shaohong Chen, Lei Wang",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5225,
"url": "https://arxiv.org/abs/2307.13365v3"
},
{
"rank": 9,
"title": "A Systematic Review of Computational Thinking in Early Ages",
"authors": "Edelberto Franco Silva, Bruno Josรฉ Dembogurski, Gustavo Silva Semaan",
"year": 2021,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.49749999999999994,
"url": "https://arxiv.org/abs/2106.10275v1"
},
{
"rank": 10,
"title": "What Aspects of Mobile Ads Do Users Care About? An Empirical Study of Mobile In-app Ad Reviews",
"authors": "Jiaping Gui, Meiyappan Nagappan, William G. J. Halfond",
"year": 2017,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.48249999999999993,
"url": "https://arxiv.org/abs/1702.07681v1"
}
],
"ranking_breakdown": [
{
"paper_id": "2302.03671",
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"total_score": 0.6425000000000001,
"query_match": 0.4,
"recency": 0.8,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2501.19323",
"title": "Trust and Trustworthiness from Human-Centered Perspective in HRI -- A Systematic Literature Review",
"total_score": 0.6075,
"query_match": 0.2125,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2509.16599",
"title": "Computational-Assisted Systematic Review and Meta-Analysis (CASMA): Effect of a Subclass of GnRH-a o",
"total_score": 0.5975,
"query_match": 0.1375,
"recency": 1.0,
"study_type_priority": 1.0,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2405.14445",
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasi",
"total_score": 0.5825,
"query_match": 0.2,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2503.02905",
"title": "Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic",
"total_score": 0.5625,
"query_match": 0.1,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.09239",
"title": "Non-invasive Techniques for Flow Rate Measurement in Water Pipes: Protocol for a Systematic Review",
"total_score": 0.5625,
"query_match": 0.1,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2204.00719",
"title": "Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review",
"total_score": 0.5425,
"query_match": 0.2,
"recency": 0.7,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2307.13365",
"title": "Pay Attention to What You Need",
"total_score": 0.5225,
"query_match": 0.2,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2106.10275",
"title": "A Systematic Review of Computational Thinking in Early Ages",
"total_score": 0.49749999999999994,
"query_match": 0.1375,
"recency": 0.6,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "1702.07681",
"title": "What Aspects of Mobile Ads Do Users Care About? An Empirical Study of Mobile In-app Ad Reviews",
"total_score": 0.48249999999999993,
"query_match": 0.4,
"recency": 0.19999999999999996,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What do systematic reviews say about attention mechanism?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: arxiv(10)\n- ์ฐ๋ ๋ฒ์: 2017-2025\n- ํ๊ท ๋ญํน ์ ์: 0.560\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก arXiv, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(7), meta_analysis(1), experimental(2)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What do systematic reviews say about attention mechanism? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: Compare unsupervised vs supervised for classification
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification",
"authors": "Sonny Achten, Francesco Tonin, Panagiotis Patrinos",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6558333333333334,
"url": "https://arxiv.org/abs/2301.13764v3"
},
{
"rank": 2,
"title": "Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images",
"authors": "Fatemeh Javadian, Zahra Aminparast, Johannes Stegmaier",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.6558333333333333,
"url": "https://arxiv.org/abs/2504.03146v1"
},
{
"rank": 3,
"title": "ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data",
"authors": "Melvin Barbaux",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6425,
"url": "https://arxiv.org/abs/2512.13228v1"
},
{
"rank": 4,
"title": "Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning",
"authors": "Matthew Gwilliam, Abhinav Shrivastava",
"year": 2022,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.6024999999999999,
"url": "https://arxiv.org/abs/2206.08347v1"
},
{
"rank": 5,
"title": "Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification",
"authors": "Jiabao Wang, Yang Li, Xiu-Shen Wei",
"year": 2022,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5958333333333333,
"url": "https://arxiv.org/abs/2203.00441v1"
},
{
"rank": 6,
"title": "Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks.",
"authors": "Parker Amanda J, Barnard Amanda S",
"year": 2021,
"venue": "Nanoscale Horiz",
"study_type": "experimental",
"score": 0.595,
"url": "https://pubmed.ncbi.nlm.nih.gov/33527922/"
},
{
"rank": 7,
"title": "Long-term foehn reconstruction combining unsupervised and supervised learning",
"authors": "Reto Stauffer, Achim Zeileis, Georg J. Mayr",
"year": 2024,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5691666666666666,
"url": "https://arxiv.org/abs/2406.01818v2"
},
{
"rank": 8,
"title": "Text classification in shipping industry using unsupervised models and Transformer based supervised models",
"authors": "Ying Xie, Dongping Song",
"year": 2022,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5624999999999999,
"url": "https://arxiv.org/abs/2212.12407v1"
},
{
"rank": 9,
"title": "Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments",
"authors": "Balagopal Unnikrishnan, Pranshu Ranjan Singh, Xulei Yang",
"year": 2020,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5558333333333333,
"url": "https://arxiv.org/abs/2006.02610v1"
},
{
"rank": 10,
"title": "Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification",
"authors": "Pranav Singh, Raviteja Chukkapalli, Shravan Chaudhari",
"year": 2023,
"venue": "arXiv",
"study_type": "other",
"score": 0.5425000000000001,
"url": "https://arxiv.org/abs/2311.10319v6"
}
],
"ranking_breakdown": [
{
"paper_id": "2301.13764",
"title": "Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification",
"total_score": 0.6558333333333334,
"query_match": 0.5333333333333333,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2504.03146",
"title": "Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear ",
"total_score": 0.6558333333333333,
"query_match": 0.5333333333333333,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.13228",
"title": "ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data",
"total_score": 0.6425,
"query_match": 0.4,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2206.08347",
"title": "Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation",
"total_score": 0.6024999999999999,
"query_match": 0.55,
"recency": 0.7,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2203.00441",
"title": "Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification",
"total_score": 0.5958333333333333,
"query_match": 0.5333333333333333,
"recency": 0.7,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "33527922",
"title": "Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural ",
"total_score": 0.595,
"query_match": 0.5,
"recency": 0.6,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2406.01818",
"title": "Long-term foehn reconstruction combining unsupervised and supervised learning",
"total_score": 0.5691666666666666,
"query_match": 0.36666666666666664,
"recency": 0.9,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2212.12407",
"title": "Text classification in shipping industry using unsupervised models and Transformer based supervised ",
"total_score": 0.5624999999999999,
"query_match": 0.44999999999999996,
"recency": 0.7,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2006.02610",
"title": "Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environm",
"total_score": 0.5558333333333333,
"query_match": 0.5333333333333333,
"recency": 0.5,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2311.10319",
"title": "Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automati",
"total_score": 0.5425000000000001,
"query_match": 0.4,
"recency": 0.8,
"study_type_priority": 0.4,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: Compare unsupervised vs supervised for classification\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: arxiv(9), pubmed(1)\n- ์ฐ๋ ๋ฒ์: 2020-2025\n- ํ๊ท ๋ญํน ์ ์: 0.598\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก arXiv, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: experimental(3), theoretical(6), other(1)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | Compare unsupervised vs supervised for classification | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What do systematic reviews say about deep learning?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"authors": "Yixuan Zhang, Joseph D Gaggiano, Nutchanon Yongsatianchot",
"year": 2023,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6425000000000001,
"url": "https://arxiv.org/abs/2302.03671v1"
},
{
"rank": 2,
"title": "Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review",
"authors": "Lisanne van Gelderen, Cristian Tejedor-Garcรญa",
"year": 2024,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6225,
"url": "https://arxiv.org/abs/2407.17844v4"
},
{
"rank": 3,
"title": "Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review",
"authors": "F. Xavier Gaya-Morey, Cristina Manresa-Yee, Jose M. Buades-Rubio",
"year": 2024,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6225,
"url": "https://arxiv.org/abs/2401.11790v3"
},
{
"rank": 4,
"title": "Trust and Trustworthiness from Human-Centered Perspective in HRI -- A Systematic Literature Review",
"authors": "Debora Firmino de Souza, Sonia Sousa, Kadri Kristjuhan-Ling",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6075,
"url": "https://arxiv.org/abs/2501.19323v1"
},
{
"rank": 5,
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study",
"authors": "Lena Schmidt, Kaitlyn Hair, Sergio Graziosi",
"year": 2024,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5825,
"url": "https://arxiv.org/abs/2405.14445v2"
},
{
"rank": 6,
"title": "Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images",
"authors": "Ajinkya Deshpande, Deep Gupta, Ankit Bhurane",
"year": 2024,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5025,
"url": "https://arxiv.org/abs/2412.03084v2"
},
{
"rank": 7,
"title": "A Systematic Review of Computational Thinking in Early Ages",
"authors": "Edelberto Franco Silva, Bruno Josรฉ Dembogurski, Gustavo Silva Semaan",
"year": 2021,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.49749999999999994,
"url": "https://arxiv.org/abs/2106.10275v1"
},
{
"rank": 8,
"title": "Deep Learning and Computational Physics (Lecture Notes)",
"authors": "Deep Ray, Orazio Pinti, Assad A. Oberai",
"year": 2023,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.48250000000000004,
"url": "https://arxiv.org/abs/2301.00942v1"
},
{
"rank": 9,
"title": "Deep Generative Models for Detector Signature Simulation: A Taxonomic Review",
"authors": "Baran Hashemi, Claudius Krause",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.48250000000000004,
"url": "https://arxiv.org/abs/2312.09597v2"
},
{
"rank": 10,
"title": "Deep Video Codec Control for Vision Models",
"authors": "Christoph Reich, Biplob Debnath, Deep Patel",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.48250000000000004,
"url": "https://arxiv.org/abs/2308.16215v6"
}
],
"ranking_breakdown": [
{
"paper_id": "2302.03671",
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"total_score": 0.6425000000000001,
"query_match": 0.4,
"recency": 0.8,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2407.17844",
"title": "Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systemati",
"total_score": 0.6225,
"query_match": 0.3,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2401.11790",
"title": "Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Sy",
"total_score": 0.6225,
"query_match": 0.3,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2501.19323",
"title": "Trust and Trustworthiness from Human-Centered Perspective in HRI -- A Systematic Literature Review",
"total_score": 0.6075,
"query_match": 0.2125,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2405.14445",
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasi",
"total_score": 0.5825,
"query_match": 0.2,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2412.03084",
"title": "Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of ",
"total_score": 0.5025,
"query_match": 0.2,
"recency": 0.9,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2106.10275",
"title": "A Systematic Review of Computational Thinking in Early Ages",
"total_score": 0.49749999999999994,
"query_match": 0.1375,
"recency": 0.6,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2301.00942",
"title": "Deep Learning and Computational Physics (Lecture Notes)",
"total_score": 0.48250000000000004,
"query_match": 0.2,
"recency": 0.8,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2312.09597",
"title": "Deep Generative Models for Detector Signature Simulation: A Taxonomic Review",
"total_score": 0.48250000000000004,
"query_match": 0.1,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2308.16215",
"title": "Deep Video Codec Control for Vision Models",
"total_score": 0.48250000000000004,
"query_match": 0.1,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What do systematic reviews say about deep learning?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: arxiv(10)\n- ์ฐ๋ ๋ฒ์: 2021-2025\n- ํ๊ท ๋ญํน ์ ์: 0.552\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก arXiv, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(6), theoretical(2), experimental(2)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What do systematic reviews say about deep learning? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the emerging topics in large language models?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Comparison of large language models in oral and maxillofacial surgery.",
"authors": "Grillo Ricardo, Llanos Alexandre Hugo, Costa Claudio",
"year": 2025,
"venue": "Br J Oral Maxillofac Surg",
"study_type": "systematic_review",
"score": 0.6972222222222223,
"url": "https://pubmed.ncbi.nlm.nih.gov/41076417/"
},
{
"rank": 2,
"title": "Small versus large bore chest tube in traumatic hemothorax, hemopneumothorax, and pneumothorax: a meta-analysis of randomized controlled trials with trial sequential analysis.",
"authors": "Granieri Stefano, Cioffi Stefano Piero Bernardo, Asaro Alessandro",
"year": 2025,
"venue": "World J Emerg Surg",
"study_type": "meta_analysis",
"score": 0.6877777777777778,
"url": "https://pubmed.ncbi.nlm.nih.gov/41287089/"
},
{
"rank": 3,
"title": "Physician Perspectives on Large Language Models in Health Care: A Cross-Sectional Survey Study.",
"authors": "Hong Hyo Jung, Shah Nigam H, Pfeffer Michael A",
"year": 2025,
"venue": "Appl Clin Inform",
"study_type": "survey",
"score": 0.6655555555555555,
"url": "https://pubmed.ncbi.nlm.nih.gov/41167595/"
},
{
"rank": 4,
"title": "Large language models in clinical trials: applications, technical advances, and future directions.",
"authors": "Lin Anqi, Wang Zhihan, Jiang Aimin",
"year": 2025,
"venue": "BMC Med",
"study_type": "theoretical",
"score": 0.6588888888888889,
"url": "https://pubmed.ncbi.nlm.nih.gov/41088200/"
},
{
"rank": 5,
"title": "Integrating clinical guidelines with large language models for improved sepsis mortality prediction.",
"authors": "Zhao Zhen, An Bo, Zhang Tianpeng",
"year": 2025,
"venue": "Health Informatics J",
"study_type": "experimental",
"score": 0.6499999999999999,
"url": "https://pubmed.ncbi.nlm.nih.gov/41195684/"
},
{
"rank": 6,
"title": "Large language models as educational collaborators: developing non-conventional teaching aids in pharmacology & therapeutics.",
"authors": "Sridharan Kannan, Sivaramakrishnan Gowri",
"year": 2025,
"venue": "BMC Med Educ",
"study_type": "theoretical",
"score": 0.6455555555555554,
"url": "https://pubmed.ncbi.nlm.nih.gov/41168789/"
},
{
"rank": 7,
"title": "Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models",
"authors": "Ju-Young Kim, Ji-Hong Park, Se-Yeon Lee",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6380555555555555,
"url": "https://arxiv.org/abs/2512.08480v1"
},
{
"rank": 8,
"title": "Evaluating Large Language Models for Sentiment Analysis and Hesitancy Analysis on Vaccine Posts From Social Media: Qualitative Study.",
"authors": "Annan Augustine, Eiden Amanda L, Wang Dong",
"year": 2025,
"venue": "JMIR Form Res",
"study_type": "theoretical",
"score": 0.6366666666666667,
"url": "https://pubmed.ncbi.nlm.nih.gov/41092067/"
},
{
"rank": 9,
"title": "Large language models for ESC guideline interpretation: a targeted review of accuracy and applicability.",
"authors": "Olariu Maria-Ecaterina, Burlacu Alexandru, Brinza Crischentian",
"year": 2025,
"venue": "Future Cardiol",
"study_type": "theoretical",
"score": 0.6366666666666667,
"url": "https://pubmed.ncbi.nlm.nih.gov/41074629/"
},
{
"rank": 10,
"title": "Assessing Large Language Models in Building a Structured Dataset From AskDocs Subreddit Data: Methodological Study.",
"authors": "Snell Quinn, Westhoff Chase, Westhoff John",
"year": 2025,
"venue": "J Med Internet Res",
"study_type": "theoretical",
"score": 0.6322222222222222,
"url": "https://pubmed.ncbi.nlm.nih.gov/41124662/"
}
],
"ranking_breakdown": [
{
"paper_id": "41076417",
"title": "Comparison of large language models in oral and maxillofacial surgery.",
"total_score": 0.6972222222222223,
"query_match": 0.45555555555555555,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41287089",
"title": "Small versus large bore chest tube in traumatic hemothorax, hemopneumothorax, and pneumothorax: a me",
"total_score": 0.6877777777777778,
"query_match": 0.34444444444444444,
"recency": 1.0,
"study_type_priority": 1.0,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41167595",
"title": "Physician Perspectives on Large Language Models in Health Care: A Cross-Sectional Survey Study.",
"total_score": 0.6655555555555555,
"query_match": 0.4888888888888888,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41088200",
"title": "Large language models in clinical trials: applications, technical advances, and future directions.",
"total_score": 0.6588888888888889,
"query_match": 0.5222222222222223,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41195684",
"title": "Integrating clinical guidelines with large language models for improved sepsis mortality prediction.",
"total_score": 0.6499999999999999,
"query_match": 0.39999999999999997,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41168789",
"title": "Large language models as educational collaborators: developing non-conventional teaching aids in pha",
"total_score": 0.6455555555555554,
"query_match": 0.4888888888888888,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.08480",
"title": "Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detectio",
"total_score": 0.6380555555555555,
"query_match": 0.38888888888888884,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "41092067",
"title": "Evaluating Large Language Models for Sentiment Analysis and Hesitancy Analysis on Vaccine Posts From",
"total_score": 0.6366666666666667,
"query_match": 0.4666666666666667,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41074629",
"title": "Large language models for ESC guideline interpretation: a targeted review of accuracy and applicabil",
"total_score": 0.6366666666666667,
"query_match": 0.4666666666666667,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41124662",
"title": "Assessing Large Language Models in Building a Structured Dataset From AskDocs Subreddit Data: Method",
"total_score": 0.6322222222222222,
"query_match": 0.4555555555555555,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the emerging topics in large language models?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(9), arxiv(1)\n- ์ฐ๋ ๋ฒ์: 2025-2025\n- ํ๊ท ๋ญํน ์ ์: 0.655\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Br J Oral Maxillofac Surg, World J Emerg Surg, Appl Clin Inform์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(1), meta_analysis(1), survey(1), theoretical(5), experimental(2)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the emerging topics in large language models? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What do systematic reviews say about attention mechanism?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"authors": "Yixuan Zhang, Joseph D Gaggiano, Nutchanon Yongsatianchot",
"year": 2023,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6425000000000001,
"url": "https://arxiv.org/abs/2302.03671v1"
},
{
"rank": 2,
"title": "Trust and Trustworthiness from Human-Centered Perspective in HRI -- A Systematic Literature Review",
"authors": "Debora Firmino de Souza, Sonia Sousa, Kadri Kristjuhan-Ling",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.6075,
"url": "https://arxiv.org/abs/2501.19323v1"
},
{
"rank": 3,
"title": "Computational-Assisted Systematic Review and Meta-Analysis (CASMA): Effect of a Subclass of GnRH-a on Endometriosis Recurrence",
"authors": "Sandro Tsang",
"year": 2025,
"venue": "arXiv",
"study_type": "meta_analysis",
"score": 0.5975,
"url": "https://arxiv.org/abs/2509.16599v3"
},
{
"rank": 4,
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study",
"authors": "Lena Schmidt, Kaitlyn Hair, Sergio Graziosi",
"year": 2024,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5825,
"url": "https://arxiv.org/abs/2405.14445v2"
},
{
"rank": 5,
"title": "Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review",
"authors": "Nicola Rossberg, Celina L. Li, Simone Innocente",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5625,
"url": "https://arxiv.org/abs/2503.02905v1"
},
{
"rank": 6,
"title": "Non-invasive Techniques for Flow Rate Measurement in Water Pipes: Protocol for a Systematic Review",
"authors": "Juan Diego Belesaca, Fabian Astudillo Salinas",
"year": 2025,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5625,
"url": "https://arxiv.org/abs/2512.09239v1"
},
{
"rank": 7,
"title": "Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review",
"authors": "Katherine Wang, Simon Julier, Youngjun Cho",
"year": 2022,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.5425,
"url": "https://arxiv.org/abs/2204.00719v1"
},
{
"rank": 8,
"title": "Pay Attention to What You Need",
"authors": "Yifei Gao, Shaohong Chen, Lei Wang",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5225,
"url": "https://arxiv.org/abs/2307.13365v3"
},
{
"rank": 9,
"title": "A Systematic Review of Computational Thinking in Early Ages",
"authors": "Edelberto Franco Silva, Bruno Josรฉ Dembogurski, Gustavo Silva Semaan",
"year": 2021,
"venue": "arXiv",
"study_type": "systematic_review",
"score": 0.49749999999999994,
"url": "https://arxiv.org/abs/2106.10275v1"
},
{
"rank": 10,
"title": "What Aspects of Mobile Ads Do Users Care About? An Empirical Study of Mobile In-app Ad Reviews",
"authors": "Jiaping Gui, Meiyappan Nagappan, William G. J. Halfond",
"year": 2017,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.48249999999999993,
"url": "https://arxiv.org/abs/1702.07681v1"
}
],
"ranking_breakdown": [
{
"paper_id": "2302.03671",
"title": "What Do We Mean When We Talk about Trust in Social Media? A Systematic Review",
"total_score": 0.6425000000000001,
"query_match": 0.4,
"recency": 0.8,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2501.19323",
"title": "Trust and Trustworthiness from Human-Centered Perspective in HRI -- A Systematic Literature Review",
"total_score": 0.6075,
"query_match": 0.2125,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2509.16599",
"title": "Computational-Assisted Systematic Review and Meta-Analysis (CASMA): Effect of a Subclass of GnRH-a o",
"total_score": 0.5975,
"query_match": 0.1375,
"recency": 1.0,
"study_type_priority": 1.0,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2405.14445",
"title": "Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasi",
"total_score": 0.5825,
"query_match": 0.2,
"recency": 0.9,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2503.02905",
"title": "Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic",
"total_score": 0.5625,
"query_match": 0.1,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.09239",
"title": "Non-invasive Techniques for Flow Rate Measurement in Water Pipes: Protocol for a Systematic Review",
"total_score": 0.5625,
"query_match": 0.1,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2204.00719",
"title": "Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review",
"total_score": 0.5425,
"query_match": 0.2,
"recency": 0.7,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2307.13365",
"title": "Pay Attention to What You Need",
"total_score": 0.5225,
"query_match": 0.2,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2106.10275",
"title": "A Systematic Review of Computational Thinking in Early Ages",
"total_score": 0.49749999999999994,
"query_match": 0.1375,
"recency": 0.6,
"study_type_priority": 0.9,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "1702.07681",
"title": "What Aspects of Mobile Ads Do Users Care About? An Empirical Study of Mobile In-app Ad Reviews",
"total_score": 0.48249999999999993,
"query_match": 0.4,
"recency": 0.19999999999999996,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What do systematic reviews say about attention mechanism?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: arxiv(10)\n- ์ฐ๋ ๋ฒ์: 2017-2025\n- ํ๊ท ๋ญํน ์ ์: 0.560\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก arXiv, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(7), meta_analysis(1), experimental(2)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What do systematic reviews say about attention mechanism? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the emerging topics in reinforcement learning?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "The future of robotic disassembly: a systematic review of techniques and applications in the age of AI.",
"authors": "Ameur Soufiane, Tabaa Mohamed, Hidila Zineb",
"year": 2025,
"venue": "Front Robot AI",
"study_type": "systematic_review",
"score": 0.655,
"url": "https://pubmed.ncbi.nlm.nih.gov/41140779/"
},
{
"rank": 2,
"title": "Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study.",
"authors": "Kalimouttou Alexandre, Kennedy Jason N, Feng Jean",
"year": 2025,
"venue": "JAMA",
"study_type": "theoretical",
"score": 0.6299999999999999,
"url": "https://pubmed.ncbi.nlm.nih.gov/40098600/"
},
{
"rank": 3,
"title": "From literature to predictive modeling: Insights and machine learning applications from in vitro comet assays related to the genotoxicity of titanium dioxide nanomaterials.",
"authors": "Furxhi Irini, Mirzaei Mahsa, Costa Anna",
"year": 2025,
"venue": "NanoImpact",
"study_type": "experimental",
"score": 0.6199999999999999,
"url": "https://pubmed.ncbi.nlm.nih.gov/40273981/"
},
{
"rank": 4,
"title": "Integrating habit science and learning theory to promote maintenance of behavior change: does adding text messages to a habit-based sleep health intervention (HABITs) improve outcomes for eveningness chronotype young adults? Study protocol for a randomized controlled trial.",
"authors": "Diaz Marlen, Ovalle Patino Estephania, Oliver Sophia",
"year": 2024,
"venue": "Trials",
"study_type": "randomized_trial",
"score": 0.615,
"url": "https://pubmed.ncbi.nlm.nih.gov/39563407/"
},
{
"rank": 5,
"title": "COVID-19 and the domestication of adolescent and young people's sexuality in the emergence of the pandemic (2020-2022).",
"authors": "Cabral Cristiane S, Paiva Vera, Barbosa Djalma",
"year": 2025,
"venue": "Cien Saude Colet",
"study_type": "survey",
"score": 0.6099999999999999,
"url": "https://pubmed.ncbi.nlm.nih.gov/41337627/"
},
{
"rank": 6,
"title": "Causal-Paced Deep Reinforcement Learning",
"authors": "Geonwoo Cho, Jaegyun Im, Doyoon Kim",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6074999999999999,
"url": "https://arxiv.org/abs/2507.02910v1"
},
{
"rank": 7,
"title": "ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives",
"authors": "Hamed Rahimi, Hubert Naacke, Camelia Constantin",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.6025,
"url": "https://arxiv.org/abs/2306.02221v1"
},
{
"rank": 8,
"title": "Simulation of human-vehicle interaction at right-turn unsignalized intersections: A game-theoretic deep maximum entropy inverse reinforcement learning method.",
"authors": "Li Wenli, Li Xianglong, Li Lingxi",
"year": 2025,
"venue": "Accid Anal Prev",
"study_type": "theoretical",
"score": 0.595,
"url": "https://pubmed.ncbi.nlm.nih.gov/39978040/"
},
{
"rank": 9,
"title": "Designing, implementing and evaluating multidisciplinary healthcare training programmes in the wartime humanitarian context of Ukraine.",
"authors": "Kivlehan Sean M, Niescierenko Michelle, Murray Kathleen",
"year": 2025,
"venue": "Emerg Med J",
"study_type": "survey",
"score": 0.595,
"url": "https://pubmed.ncbi.nlm.nih.gov/39746801/"
},
{
"rank": 10,
"title": "Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.",
"authors": "Chen Yuheng, Montes McNeil Alexander, Park Taehyuk",
"year": 2025,
"venue": "Nanophotonics",
"study_type": "experimental",
"score": 0.59,
"url": "https://pubmed.ncbi.nlm.nih.gov/41246485/"
}
],
"ranking_breakdown": [
{
"paper_id": "41140779",
"title": "The future of robotic disassembly: a systematic review of techniques and applications in the age of ",
"total_score": 0.655,
"query_match": 0.35,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40098600",
"title": "Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study.",
"total_score": 0.6299999999999999,
"query_match": 0.45,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40273981",
"title": "From literature to predictive modeling: Insights and machine learning applications from in vitro com",
"total_score": 0.6199999999999999,
"query_match": 0.325,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "39563407",
"title": "Integrating habit science and learning theory to promote maintenance of behavior change: does adding",
"total_score": 0.615,
"query_match": 0.3125,
"recency": 0.9,
"study_type_priority": 0.8,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41337627",
"title": "COVID-19 and the domestication of adolescent and young people's sexuality in the emergence of the pa",
"total_score": 0.6099999999999999,
"query_match": 0.35,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2507.02910",
"title": "Causal-Paced Deep Reinforcement Learning",
"total_score": 0.6074999999999999,
"query_match": 0.3125,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2306.02221",
"title": "ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives",
"total_score": 0.6025,
"query_match": 0.4,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "39978040",
"title": "Simulation of human-vehicle interaction at right-turn unsignalized intersections: A game-theoretic d",
"total_score": 0.595,
"query_match": 0.3625,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "39746801",
"title": "Designing, implementing and evaluating multidisciplinary healthcare training programmes in the warti",
"total_score": 0.595,
"query_match": 0.3125,
"recency": 1.0,
"study_type_priority": 0.6,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "41246485",
"title": "Machine-learning-assisted photonic device development: a multiscale approach from theory to characte",
"total_score": 0.59,
"query_match": 0.2875,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the emerging topics in reinforcement learning?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(8), arxiv(2)\n- ์ฐ๋ ๋ฒ์: 2023-2025\n- ํ๊ท ๋ญํน ์ ์: 0.612\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Front Robot AI, JAMA, NanoImpact์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(1), theoretical(2), experimental(4), randomized_trial(1), survey(2)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the emerging topics in reinforcement learning? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: How has neural networks evolved over the past 5 years?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Review of applications of deep learning in veterinary diagnostics and animal health.",
"authors": "Xiao Sam, Dhand Navneet K, Wang Zhiyong",
"year": 2025,
"venue": "Front Vet Sci",
"study_type": "systematic_review",
"score": 0.5870000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40144529/"
},
{
"rank": 2,
"title": "Multiclass Graph-Based Large Margin Classifiers: Unified Approach for Support Vectors and Neural Networks",
"authors": "Vรญtor M. Hanriot, Luiz C. B. Torres, Antรดnio P. Braga",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5585,
"url": "https://arxiv.org/abs/2512.13410v1"
},
{
"rank": 3,
"title": "Tackling the Curse of Dimensionality with Physics-Informed Neural Networks",
"authors": "Zheyuan Hu, Khemraj Shukla, George Em Karniadakis",
"year": 2023,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5345,
"url": "https://arxiv.org/abs/2307.12306v6"
},
{
"rank": 4,
"title": "The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory",
"authors": "Sergey Oladyshkin, Timothy Praditia, Ilja Krรถker",
"year": 2023,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5185,
"url": "https://arxiv.org/abs/2306.14753v1"
},
{
"rank": 5,
"title": "Newton-Puiseux Analysis for Interpretability and Calibration of Complex-Valued Neural Networks",
"authors": "Piotr Migus",
"year": 2025,
"venue": "arXiv",
"study_type": "theoretical",
"score": 0.5185,
"url": "https://arxiv.org/abs/2504.19176v2"
},
{
"rank": 6,
"title": "Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks",
"authors": "Danny D'Agostino, Ilija Ilievski, Christine Annette Shoemaker",
"year": 2023,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5065,
"url": "https://arxiv.org/abs/2307.05639v2"
},
{
"rank": 7,
"title": "Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.",
"authors": "Gardezi Syed Jamal Safdar, Elazab Ahmed, Lei Baiying",
"year": 2019,
"venue": "J Med Internet Res",
"study_type": "systematic_review",
"score": 0.506,
"url": "https://pubmed.ncbi.nlm.nih.gov/31350843/"
},
{
"rank": 8,
"title": "MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks",
"authors": "Xinyu Fu, Irwin King",
"year": 2022,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.49849999999999994,
"url": "https://arxiv.org/abs/2211.12792v2"
},
{
"rank": 9,
"title": "Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation",
"authors": "Giorgio Morales, John W. Sheppard",
"year": 2022,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.49049999999999994,
"url": "https://arxiv.org/abs/2212.06370v4"
},
{
"rank": 10,
"title": "Continual Learning for Recurrent Neural Networks: an Empirical Evaluation",
"authors": "Andrea Cossu, Antonio Carta, Vincenzo Lomonaco",
"year": 2021,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.4784999999999999,
"url": "https://arxiv.org/abs/2103.07492v4"
}
],
"ranking_breakdown": [
{
"paper_id": "40144529",
"title": "Review of applications of deep learning in veterinary diagnostics and animal health.",
"total_score": 0.5870000000000001,
"query_match": 0.18,
"recency": 1.0,
"study_type_priority": 0.9,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2512.13410",
"title": "Multiclass Graph-Based Large Margin Classifiers: Unified Approach for Support Vectors and Neural Net",
"total_score": 0.5585,
"query_match": 0.19,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2307.12306",
"title": "Tackling the Curse of Dimensionality with Physics-Informed Neural Networks",
"total_score": 0.5345,
"query_match": 0.32999999999999996,
"recency": 0.8,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2306.14753",
"title": "The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could bene",
"total_score": 0.5185,
"query_match": 0.29000000000000004,
"recency": 0.8,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2504.19176",
"title": "Newton-Puiseux Analysis for Interpretability and Calibration of Complex-Valued Neural Networks",
"total_score": 0.5185,
"query_match": 0.19,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2307.05639",
"title": "Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Ne",
"total_score": 0.5065,
"query_match": 0.16,
"recency": 0.8,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "31350843",
"title": "Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.",
"total_score": 0.506,
"query_match": 0.24,
"recency": 0.4,
"study_type_priority": 0.9,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2211.12792",
"title": "MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks",
"total_score": 0.49849999999999994,
"query_match": 0.19,
"recency": 0.7,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2212.06370",
"title": "Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation",
"total_score": 0.49049999999999994,
"query_match": 0.16999999999999998,
"recency": 0.7,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "2103.07492",
"title": "Continual Learning for Recurrent Neural Networks: an Empirical Evaluation",
"total_score": 0.4784999999999999,
"query_match": 0.19,
"recency": 0.6,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: How has neural networks evolved over the past 5 years?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(2), arxiv(8)\n- ์ฐ๋ ๋ฒ์: 2019-2025\n- ํ๊ท ๋ญํน ์ ์: 0.520\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Front Vet Sci, arXiv, arXiv์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: systematic_review(2), experimental(5), theoretical(3)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | How has neural networks evolved over the past 5 years? | 10 |
๋ค์ ์ง๋ฌธ์ ๋ํด ๋
ผ๋ฌธ์ ๊ฒ์ํ๊ณ ๋ญํนํ์ฌ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ์ธ์.
์ง๋ฌธ: What are the differences between LSTM and RNN?
์ถ๋ ฅ ํ์:
1. Evidence Table (Top-K): ๊ฒ์๋ ๋
ผ๋ฌธ์ ์์ K๊ฐ๋ฅผ ํ
์ด๋ธ ํ์์ผ๋ก ์ ์
2. Ranking Breakdown: ๊ฐ ๋
ผ๋ฌธ์ ๋ญํน ์ ์ ๊ตฌ์ฑ ์์๋ฅผ ๊ณต๊ฐ
3. Answer Summary: ๊ฒ์ ๊ฒฐ๊ณผ๋ฅผ ์์ฝํ์ฌ ์ ์
4. Charts: ์ฐ๋๋ณ ํธ๋ ๋, ์ ๋ ๋ถํฌ, ์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ์ ๋ํ ์ค๋ช
๋ชจ๋ ์ฃผ์ฅ์ Evidence Table์ ๋
ผ๋ฌธ์ ๊ทผ๊ฑฐํด์ผ ํ๋ฉฐ, ๋ญํน ๊ทผ๊ฑฐ๋ฅผ ๋ช
์ํด์ผ ํฉ๋๋ค. | {
"evidence_table": [
{
"rank": 1,
"title": "Carbon market price prediction in the Yangtze River Basin based on improved deep learning ensemble model with CEEMDAN and Attention-RNN.",
"authors": "Lu Zuliang, Cao Zhihui, Xiang Zhuran",
"year": 2025,
"venue": "Carbon Balance Manag",
"study_type": "experimental",
"score": 0.625,
"url": "https://pubmed.ncbi.nlm.nih.gov/41269474/"
},
{
"rank": 2,
"title": "Developing and validating machine learning models to predict next-day extubation.",
"authors": "Fenske Samuel W, Peltekian Alec, Kang Mengjia",
"year": 2025,
"venue": "Sci Rep",
"study_type": "randomized_trial",
"score": 0.6100000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40731125/"
},
{
"rank": 3,
"title": "Predicting Synergistic Drug Combinations Based on Fusion of Cell and Drug Molecular Structures.",
"authors": "Yan Shiyu, Yu Gang, Yang Jiaoxing",
"year": 2025,
"venue": "Interdiscip Sci",
"study_type": "experimental",
"score": 0.605,
"url": "https://pubmed.ncbi.nlm.nih.gov/40088336/"
},
{
"rank": 4,
"title": "Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach.",
"authors": "Khalid Madiha, Mushtaq Muhammad Faheem, Akram Urooj",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5900000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40754634/"
},
{
"rank": 5,
"title": "Performance analysis of neural network architectures for time series forecasting: A comparative study of RNN, LSTM, GRU, and hybrid models.",
"authors": "Yunita Ariana, Pratama Mhd Iqbal, Almuzakki Muhammad Zaki",
"year": 2025,
"venue": "MethodsX",
"study_type": "theoretical",
"score": 0.5850000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40777584/"
},
{
"rank": 6,
"title": "A Long Short-Term Memory (LSTM) Model for Business Sentiment Analysis Based on Recurrent Neural Network",
"authors": "Md. Jahidul Islam Razin, Md. Abdul Karim, M. F. Mridha",
"year": 2025,
"venue": "arXiv",
"study_type": "experimental",
"score": 0.5825,
"url": "https://arxiv.org/abs/2509.03060v1"
},
{
"rank": 7,
"title": "Development of student intent-based educational chatbot system with adaptive and attentive DTCN on symmetric convolution approach.",
"authors": "Kathole Atul, Patil Suvarna, Jadhav Dr Devyani",
"year": 2025,
"venue": "MethodsX",
"study_type": "experimental",
"score": 0.5750000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40822546/"
},
{
"rank": 8,
"title": "Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning.",
"authors": "Gulzar Qawsar, Mustafa Khurram",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5750000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40695948/"
},
{
"rank": 9,
"title": "Comparative analysis and enhancing rainfall prediction models for monthly rainfall prediction in the Eastern Thailand.",
"authors": "Chuasuk Preeyanuch, Bhatrasataponkul Tachanat, Akkarapongtrakul Aniruj",
"year": 2025,
"venue": "MethodsX",
"study_type": "theoretical",
"score": 0.575,
"url": "https://pubmed.ncbi.nlm.nih.gov/39802428/"
},
{
"rank": 10,
"title": "Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks.",
"authors": "Almadhor Ahmad, Alsubai Shtwai, Bouazzi Imen",
"year": 2025,
"venue": "Sci Rep",
"study_type": "experimental",
"score": 0.5650000000000001,
"url": "https://pubmed.ncbi.nlm.nih.gov/40102534/"
}
],
"ranking_breakdown": [
{
"paper_id": "41269474",
"title": "Carbon market price prediction in the Yangtze River Basin based on improved deep learning ensemble m",
"total_score": 0.625,
"query_match": 0.375,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40731125",
"title": "Developing and validating machine learning models to predict next-day extubation.",
"total_score": 0.6100000000000001,
"query_match": 0.25,
"recency": 1.0,
"study_type_priority": 0.8,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40088336",
"title": "Predicting Synergistic Drug Combinations Based on Fusion of Cell and Drug Molecular Structures.",
"total_score": 0.605,
"query_match": 0.2875,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40754634",
"title": "Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybri",
"total_score": 0.5900000000000001,
"query_match": 0.25,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.9999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40777584",
"title": "Performance analysis of neural network architectures for time series forecasting: A comparative stud",
"total_score": 0.5850000000000001,
"query_match": 0.375,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "2509.03060",
"title": "A Long Short-Term Memory (LSTM) Model for Business Sentiment Analysis Based on Recurrent Neural Netw",
"total_score": 0.5825,
"query_match": 0.25,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.95,
"duplicate_penalty": 0.0
},
{
"paper_id": "40822546",
"title": "Development of student intent-based educational chatbot system with adaptive and attentive DTCN on s",
"total_score": 0.5750000000000001,
"query_match": 0.25,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40695948",
"title": "Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems us",
"total_score": 0.5750000000000001,
"query_match": 0.25,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "39802428",
"title": "Comparative analysis and enhancing rainfall prediction models for monthly rainfall prediction in the",
"total_score": 0.575,
"query_match": 0.35,
"recency": 1.0,
"study_type_priority": 0.5,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
},
{
"paper_id": "40102534",
"title": "Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks.",
"total_score": 0.5650000000000001,
"query_match": 0.22499999999999998,
"recency": 1.0,
"study_type_priority": 0.7,
"evidence_completeness": 0.8999999999999999,
"duplicate_penalty": 0.0
}
],
"answer_summary": "๊ฒ์ ์ง๋ฌธ: What are the differences between LSTM and RNN?\n\n๊ฒ์ ๊ฒฐ๊ณผ ์์ฝ:\n- ์ด 10๊ฐ์ ๋
ผ๋ฌธ์ด ๊ฒ์๋์์ต๋๋ค.\n- ์์ค๋ณ ๋ถํฌ: pubmed(9), arxiv(1)\n- ์ฐ๋ ๋ฒ์: 2025-2025\n- ํ๊ท ๋ญํน ์ ์: 0.589\n\n์ฃผ์ ๋ฐ๊ฒฌ:\n- ์์ ๋
ผ๋ฌธ๋ค์ ์ฃผ๋ก Carbon Balance Manag, Sci Rep, Interdiscip Sci์์ ๋ฐํ๋์์ต๋๋ค.\n- ์ฐ๊ตฌ ์ ํ ๋ถํฌ: experimental(7), randomized_trial(1), theoretical(2)",
"charts": {
"year_trend": "์ฐ๋๋ณ ํธ๋ ๋ ์ฐจํธ",
"venue_distribution": "์ ๋ ๋ถํฌ ์ฐจํธ",
"study_type_distribution": "์ฐ๊ตฌ ์ ํ ๋ถํฌ ์ฐจํธ"
}
} | What are the differences between LSTM and RNN? | 10 |
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