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Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities.
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., Amodei, D., 2020 · 2001
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Language models are few-shot learners
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D., 2020 · 2005
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The probabilistic relevance framework: BM25 and beyond
Robertson, S.E., Zaragoza, H., 2009 · 2009
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Reading wikipedia to answer open-domain questions, in: Barzilay, R., Kan, M. (Eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, Association for Computational Linguistics. pp. 1870–1879
Chen, D., Fisch, A., Weston, J., Bordes, A., 2017 · 2017
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BERT: pre-training of deep bidirectional transformers for language understanding, in: Burstein, J., Doran, C., Solorio, T. (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Association for Computational Linguistics. pp. 4171–4186
Devlin, J., Chang, M., Lee, K., Toutanova, K., 2019 · 2019
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End-to-end open-domain question answering with bertserini, in: Ammar, W., Louis, A., Mostafazadeh, N. (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Demonstrations, Association for Computational Linguistics. pp. 72–77
Yang, W., Xie, Y., Lin, A., Li, X., Tan, L., Xiong, K., Li, M., Lin, J., 2019 · 2019
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Retrieval augmented language model pre-training, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, PMLR. pp. 3929–3938
Guu, K., Lee, K., Tung, Z., Pasupat, P., Chang, M., 2020 · 2020
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Dense passage retrieval for open-domain question answering, in: Webber, B., Cohn, T., He, Y., Liu, Y. (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics. pp. 6769–6781
Karpukhin, V., Oguz, B., Min, S., Lewis, P.S.H., Wu, L., Edunov, S., Chen, D., Yih, W., 2020 · 2020
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H.P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F.P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W.H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A.N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., Zaremba, W., 2021 · 2021
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Fool me twice: Entailment from wikipedia gamification, in: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tür, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Association for Computational Linguistics. pp. 352–365
Eisenschlos, J., Dhingra, B., Bulian, J., Börschinger, B., Boyd-Graber, J.L., 2021 · 2021
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Open domain question answering over tables via dense retrieval, in: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tür, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Association for Computational Linguistics. pp. 512–519
Herzig, J., Müller, T., Krichene, S., Eisenschlos, J., 2021 · 2021
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Learning transferable visual models from natural language supervision, in: Meila, M., Zhang, T. (Eds.), Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, PMLR. pp. 8748–8763
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I., 2021 · 2021
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A.M., Pillai, T.S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., Fiedel, N., 2022 · 2022
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Few-shot learning with retrieval augmented language models
Izacard, G., Lewis, P.S.H., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., Grave, E., 2022 · 2022
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Large language models are zero-shot reasoners, in: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (Eds.), Advances in Neural Information Processing Systems
Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y., 2022 · 2022
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Internet-augmented language models through few-shot prompting for open-domain question answering
Lazaridou, A., Gribovskaya, E., Stokowiec, W., Grigorev, N., 2022 · 2022
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LlamaIndex
Liu, J., 2022 · 2022
Earlier work this paper cites.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K., Zhu, S., Tafjord, O., Clark, P., Kalyan, A., 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R., 2022 · 2022
Cited alongside, same era.
Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering, in: Flores, G., Chen, G.H., Pollard, T.J., Ho, J.C., Naumann, T. (Eds.), Conference on Health, Inference, and Learning, CHIL 2022, 7-8 April 2022, Virtual Event, PMLR. pp. 248–260
Pal, A., Umapathi, L.K., Sankarasubbu, M., 2022 · 2022
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization, in: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net
Evaluating GPT-4 and chatgpt on japanese medical licensing examinations
Kasai, J., Kasai, Y., Sakaguchi, K., Yamada, Y., Radev, D., 2023 · 2023
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Chameleon: Plug-and-play compositional reasoning with large language models
Lu, P., Peng, B., Cheng, H., Galley, M., Chang, K., Wu, Y.N., Zhu, S., Gao, J., 2023 · 2023
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Augmented language models: a survey
Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., Grave, E., LeCun, Y., Scialom, T., 2023 · 2023
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New bing
Microsoft, 2023 · 2023
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Do large language models understand chemistry? a conversation with chatgpt
Nascimento, C., Monteiro, C., Pimentel, Silva, A., 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sanh, V., Webson, A., Raffel, C., Bach, S.H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Raja, A., Dey, M., Bari, M.S., Xu, C., Thakker, U., Sharma, S.S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N.V., Datta, D., Chang, J., Jiang, M.T., Wang, H., Manica, M., Shen, S., Yong, Z.X., Pandey, H., Bawden, R., Wang, T., Neeraj, T., Rozen, J., Sharma, A., Santilli, A., Févry, T., Fries, J.A., Teehan, R., Scao, T.L., Biderman, S., Gao, L., Wolf, T., Rush, A.M., 2022 · 2022
Cited alongside, same era.
BLOOM: A 176b-parameter open-access multilingual language model
Scao, T.L., Fan, A., Akiki, C., Pavlick, E., Ilic, S., Hesslow, D., Castagné, R., Luccioni, A.S., Yvon, F., Gallé, M., Tow, J., Rush, A.M., Biderman, S., Webson, A., Ammanamanchi, P.S., Wang, T., Sagot, B., Muennighoff, N., del Moral, A.V., Ruwase, O., Bawden, R., Bekman, S., McMillan-Major, A., Beltagy, I., Nguyen, H., Saulnier, L., Tan, S., Suarez, P.O., Sanh, V., Laurençon, H., Jernite, Y., Launay, J., Mitchell, M., Raffel, C., Gokaslan, A., Simhi, A., Soroa, A., Aji, A.F., Alfassy, A., Rogers, A., Nitzav, A.K., Xu, C., Mou, C., Emezue, C., Klamm, C., Leong, C., van Strien, D., Adelani, D.I., et al., 2022 · 2022
Cited alongside, same era.
Language models are multilingual chain-of-thought reasoners
Shi, F., Suzgun, M., Freitag, M., Wang, X., Srivats, S., Vosoughi, S., Chung, H.W., Tay, Y., Ruder, S., Zhou, D., Das, D., Wei, J., 2022 · 2022
Cited alongside, same era.
Finetuned language models are zero-shot learners, in: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net
Wei, J., Bosma, M., Zhao, V.Y., Guu, K., Yu, A.W., Lester, B., Du, N., Dai, A.M., Le, Q.V., 2022a · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models, in: NeurIPS
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E.H., Le, Q.V., Zhou, D., 2022c · 2022
Cited alongside, same era.
Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models, in: Goldberg, Y., Kozareva, Z., Zhang, Y. (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, Association for Computational Linguistics. pp. 602–631
Xie, T., Wu, C.H., Shi, P., Zhong, R., Scholak, T., Yasunaga, M., Wu, C., Zhong, M., Yin, P., Wang, S.I., Zhong, V., Wang, B., Li, C., Boyle, C., Ni, A., Yao, Z., Radev, D., Xiong, C., Kong, L., Zhang, R., Smith, N.A., Zettlemoyer, L., Yu, T., 2022 · 2022
Cited alongside, same era.
Zeroprompt: Scaling prompt-based pretraining to 1, 000 tasks improves zero-shot generalization, in: Goldberg, Y., Kozareva, Z., Zhang, Y. (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, Association for Computational Linguistics. pp. 4235–4252
Xu, H., Chen, Y., Du, Y., Shao, N., Wang, Y., Li, H., Yang, Z., 2022 · 2022
Cited alongside, same era.
OPT: open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M.T., Li, X., Lin, X.V., Mihaylov, T., Ott, M., Shleifer, S., Shuster, K., Simig, D., Koura, P.S., Sridhar, A., Wang, T., Zettlemoyer, L., 2022 · 2022
Cited alongside, same era.
Chatgpt banned in italy over privacy concerns
BBC, 2023 · 2023
Cited alongside, same era.
Chatgpt plugins
OpenAI, 2023a · 2023
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In-context retrieval-augmented language models
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., Shoham, Y., 2023 · 2023
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Hugginggpt: Solving AI tasks with chatgpt and its friends in huggingface
Shen, Y., Song, K., Tan, X., Li, D., Lu, W., Zhuang, Y., 2023 · 2023
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REPLUG: retrieval-augmented black-box language models
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., Yih, W., 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B., 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G., 2023 · 2023
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AI and the FCI: can chatgpt project an understanding of introductory physics?
West, C.G., 2023 · 2023
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Visual chatgpt: Talking, drawing and editing with visual foundation models
Wu, C., Yin, S., Qi, W., Wang, X., Tang, Z., Duan, N., 2023 · 2023
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Wizardlm: Empowering large language models to follow complex instructions
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., Jiang, D., 2023 · 2023
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Exploring the limits of chatgpt for query or aspect-based text summarization
Yang, X., Li, Y., Zhang, X., Chen, H., Cheng, W., 2023 · 2023
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Generate rather than retrieve: Large language models are strong context generators, in: The Eleventh International Conference on Learning Representations
Yu, W., Iter, D., Wang, S., Xu, Y., Ju, M., Sanyal, S., Zhu, C., Zeng, M., Jiang, M., 2023 · 2023
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Multimodal chain-of-thought reasoning in language models
Zhang, Z., Zhang, A., Li, M., Zhao, H., Karypis, G., Smola, A., 2023 · 2023
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