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Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis.
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) · 1901
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Giving bert a calculator: Finding operations and arguments with reading comprehension
Andor, D., He, L., Lee, K., and Pitler, E. (2019) · 1909
Earlier work this paper cites.
Exploiting cloze questions for few shot text classification and natural language inference
Schick, T. and Schütze, H. (2020) · 2001
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Unsupervised translation of programming languages
Lachaux, M.-A., Roziere, B., Chanussot, L., and Lample, G. (2020) · 2006
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A machine learning framework for programming by example
Menon, A., Tamuz, O., Gulwani, S., Lampson, B., and Kalai, A. (2013) · 2013
Earlier work this paper cites.
Robustfill: Neural program learning under noisy i/o
Devlin, J., Uesato, J., Bhupatiraju, S., Singh, R., Mohamed, A.-r., and Kohli, P. (2017) · 2017
Earlier work this paper cites.
Autopandas: neural-backed generators for program synthesis
Bavishi, R., Lemieux, C., Fox, R., Sen, K., and Stoica, I. (2019) · 2019
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al. (2021) · 2021
Earlier work this paper cites.
Internet-augmented dialogue generation
Komeili, M., Shuster, K., and Weston, J. (2021) · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al. (2021) · 2021
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Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · 2021
Earlier work this paper cites.
Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., et al. (2022) · 2022
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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., et al. (2022) · 2022
Earlier work this paper cites.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · 2022
Earlier work this paper cites.
Pal: Program-aided language models
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., and Neubig, G. (2022) · 2022
Cited alongside, same era.
Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Iyer, S., Lin, X. V., Pasunuru, R., Mihaylov, T., Simig, D., Yu, P., Shuster, K., Wang, T., Liu, Q., Koura, P. S., et al. (2022) · 2022
Cited alongside, same era.
Jigsaw: Large language models meet program synthesis
Jain, N., Vaidyanath, S., Iyer, A., Natarajan, N., Parthasarathy, S., Rajamani, S., and Sharma, R. (2022) · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y. (2022) · 2022
Cited alongside, same era.
Internet-augmented language models through few-shot prompting for open-domain question answering
Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · 2022
Later among the works it cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · 2023
Closest in time.
Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D. (2023) · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P. (2023) · 2023
Closest in time.
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Lazaridou, A., Gribovskaya, E., Stokowiec, W., and Grigorev, N. (2022) · 2022
Cited alongside, same era.
Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · 2022
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C. (2022) · 2022
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., et al. (2022) · 2022
Cited alongside, same era.
Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage
Shuster, K., Xu, J., Komeili, M., Ju, D., Smith, E. M., Roller, S., Ung, M., Chen, M., Arora, K., Lane, J., et al. (2022) · 2022
Cited alongside, same era.
Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · 2022
Cited alongside, same era.
Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Naik, A., Ashok, A., Dhanasekaran, A. S., Arunkumar, A., Stap, D., et al. (2022b) · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D. (2022) · 2022
Cited alongside, same era.
Kim, G., Baldi, P., and McAleer, S. (2023) · 2023
Closest in time.
Starcoder: may the source be with you!
Li, R., Allal, L. B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Li, J., Chim, J., et al. (2023) · 2023
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Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis
Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al. (2023) · 2023
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Codegen2: Lessons for training llms on programming and natural languages
Nijkamp, E., Hayashi, H., Xiong, C., Savarese, S., and Zhou, Y. (2023) · 2023
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Gpt-4 technical report
OpenAI (2023) · 2023
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T. (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., and Zhuang, Y. (2023) · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
Shinn, N., Labash, B., and Gopinath, A. (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., and 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.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · 2023
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Chatgpt for robotics: Design principles and model abilities
Vemprala, S., Bonatti, R., Bucker, A., and Kapoor, A. (2023) · 2023
Closest in time.