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Recent advancements in large language models (LLMs) underscore their potential for responding to inquiries in various domains.
Cosmos QA: machine reading comprehension with contextual commonsense reasoning
Huang, L., Bras, R. L., Bhagavatula, C., and Choi, Y · 1909
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Pubmedqa: A dataset for biomedical research question answering
Jin, Q., Dhingra, B., Liu, Z., Cohen, W. W., and Lu, X · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., and Szolovits, P · 2021
Earlier work this paper cites.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Earlier work this paper cites.
Can large language models reason about medical questions?
Liévin, V., Hother, C. E., and Winther, O · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Earlier work this paper cites.
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al · 2022
Earlier work this paper cites.
Galactica: A large language model for science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
Cited alongside, same era.
Chateval: Towards better llm-based evaluators through multi-agent debate
Chan, C.-M., Chen, W., Su, Y., Yu, J., Xue, W., Zhang, S., Fu, J., and Liu, Z · 2023
Cited alongside, same era.
Improving factuality and reasoning in language models through multiagent debate
GPT-4 Technical Report
OpenAI · 2023
Closest in time.
Learning gain differences between chatgpt and human tutor generated algebra hints
Pardos, Z. A. and Bhandari, S · 2023
Closest in time.
Generative agents: Interactive simulacra of human behavior
Park, J. S., O’Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S · 2023
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Gpqa: A graduate-level google-proof q&a benchmark, 2023
Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, R. Y., Dirani, J., Michael, J., and Bowman, S. R · 2023
Closest in time.
Towards expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al · 2023
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Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I · 2023
Cited alongside, same era.
Improving language model negotiation with self-play and in-context learning from ai feedback
Fu, Y., Peng, H., Khot, T., and Lapata, M · 2023
Cited alongside, same era.
Medalpaca–an open-source collection of medical conversational ai models and training data
Han, T., Adams, L. C., Papaioannou, J.-M., Grundmann, P., Oberhauser, T., Löser, A., Truhn, D., and Bressem, K. K · 2023
Cited alongside, same era.
Large language models in law: A survey, 2023
Lai, J., Gan, W., Wu, J., Qi, Z., and Yu, P. S · 2023
Cited alongside, same era.
Camel: Communicative agents for ”mind” exploration of large scale language model society
Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., and Ghanem, B · 2023
Cited alongside, same era.
Encouraging divergent thinking in large language models through multi-agent debate
Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Tu, Z., and Shi, S · 2023
Cited alongside, same era.
Can generalist foundation models outcompete special-purpose tuning? case study in medicine, 2023
Nori, H., Lee, Y. T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., King, N., Larson, J., Li, Y., Liu, W., Luo, R., McKinney, S. M., Ness, R. O., Poon, H., Qin, T., Usuyama, N., White, C., and Horvitz, E · 2023
Cited alongside, same era.
Clinicalgpt: Large language models finetuned with diverse medical data and comprehensive evaluation
Wang, G., Yang, G., Du, Z., Fan, L., and Li, X
Cited in the paper.
Zhang, Y., Zhang, F., Yang, Z., and Wang, Z · 2023
Closest in time.
Chess state tracking, 2021
bench Team, B · 2024
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Mixtral of experts, 2024
Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., de las Casas, D., Hanna, E. B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L. R., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T. L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2024
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Impact of guidance and interaction strategies for llm use on learner performance and perception, 2024
Kumar, H., Musabirov, I., Reza, M., Shi, J., Wang, X., Williams, J. J., Kuzminykh, A., and Liut, M · 2024
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Gpt-4 technical report, 2024
OpenAI et al · 2024
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