Toward expert-level medical question answering with large language models K Singhal, T Tu, J Gottweis, R Sayres, E Wulczyn, M Amin, L Hou, K Clark, ... Nature Medicine, 1-8, 2025 | 723 | 2025 |
Rlaif: Scaling reinforcement learning from human feedback with ai feedback H Lee, S Phatale, H Mansoor, KR Lu, T Mesnard, J Ferret, C Bishop, ... | 457 | 2023 |
Towards generalist biomedical AI T Tu, S Azizi, D Driess, M Schaekermann, M Amin, PC Chang, A Carroll, ... NEJM AI 1 (3), AIoa2300138, 2024 | 333 | 2024 |
Federated reconstruction: Partially local federated learning K Singhal, H Sidahmed, Z Garrett, S Wu, J Rush, S Prakash Advances in Neural Information Processing Systems 34, 11220-11232, 2021 | 154 | 2021 |
Automatic suggestions for message exchange threads TS Milligan, H Shemer, D Kiilerich, G Ji, O Gershony, S Nazarov, ... US Patent 10,530,723, 2020 | 147 | 2020 |
Learning to merge word senses R Snow, S Prakash, D Jurafsky, AY Ng Proceedings of the 2007 joint conference on empirical methods in natural …, 2007 | 120 | 2007 |
Towards accurate differential diagnosis with large language models D McDuff, M Schaekermann, T Tu, A Palepu, A Wang, J Garrison, ... arXiv preprint arXiv:2312.00164, 2023 | 71 | 2023 |
Universal self-consistency for large language model generation X Chen, R Aksitov, U Alon, J Ren, K Xiao, P Yin, S Prakash, C Sutton, ... arXiv preprint arXiv:2311.17311, 2023 | 59 | 2023 |
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback H Lee, S Phatale, H Mansoor, T Mesnard, J Ferret, KR Lu, C Bishop, ... Forty-first International Conference on Machine Learning, 0 | 52 | |
y Arcas K Singhal, T Tu, J Gottweis, R Sayres, E Wulczyn, L Hou, K Clark, S Pfohl, ... BA, Tomasev, N., Liu, Y., Wong, R., Semturs, C., Mahdavi, SS, Barral, J …, 2022 | 43 | 2022 |
Towards expert-level medical question answering with large language models. 2023 K Singhal, T Tu, J Gottweis, R Sayres, E Wulczyn, L Hou, K Clark, S Pfohl, ... arXiv preprint arXiv:2305.09617, 2023 | 39 | 2023 |
Rest meets react: Self-improvement for multi-step reasoning llm agent R Aksitov, S Miryoosefi, Z Li, D Li, S Babayan, K Kopparapu, Z Fisher, ... arXiv preprint arXiv:2312.10003, 2023 | 30 | 2023 |
A toolbox for surfacing health equity harms and biases in large language models SR Pfohl, H Cole-Lewis, R Sayres, D Neal, M Asiedu, A Dieng, ... Nature Medicine 30 (12), 3590-3600, 2024 | 29 | 2024 |
User-LLM: Efficient LLM Contextualization with User Embeddings L Ning, L Liu, J Wu, N Wu, D Berlowitz, S Prakash, B Green, S O'Banion, ... arXiv preprint arXiv:2402.13598, 2024 | 23 | 2024 |
S. PRAKASH O Prakash, A Shrivastava Indian J. Pure Appl. Phys 24, 306, 1986 | 19* | 1986 |
Learning to merge word senses RSS Prakash, D Jurafsky, AY Ng Computer Science Department, Stanford University, 2007 | 18 | 2007 |
Learning federated representations and recommendations with limited negatives L Ning, K Singhal, EX Zhou, S Prakash arXiv preprint arXiv:2108.07931, 2021 | 13 | 2021 |
Towards generalist biomedical ai, 2023 T Tu, S Azizi, D Driess, M Schaekermann, M Amin, PC Chang, A Carroll, ... URL https://arxiv. org/abs/2307.14334, 2023 | 11 | 2023 |
Augmentations vs algorithms: What works in self-supervised learning W Morningstar, A Bijamov, C Duvarney, L Friedman, N Kalibhat, L Liu, ... arXiv preprint arXiv:2403.05726, 2024 | 8 | 2024 |
Toward interpretability of dual-encoder models for dialogue response suggestions Y Li, D Li, S Prakash, P Wang arXiv preprint arXiv:2003.04998, 2020 | 3 | 2020 |