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Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents.
Textworld: A learning environment for text-based games
Côté, M.-A., Kádár, A., Yuan, X., Kybartas, B., Barnes, T., Fine, E., Moore, J., Hausknecht, M., El Asri, L., Adada, M., et al. (2019) · 2018
Earlier work this paper cites.
Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
Earlier work this paper cites.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., and Manning, C. D. (2018) · 2018
Earlier work this paper cites.
A large-scale longitudinal study of flaky tests
Lam, W., Winter, S., Wei, A., Xie, T., Marinov, D., and Bell, J. (2020) · 2020
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al. (2021) · 2021
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.
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
Earlier work this paper cites.
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Shridhar, M., Yuan, X., Côté, M.-A., Bisk, Y., Trischler, A., and Hausknecht, M. (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
Earlier work this paper cites.
Brooks, E., Walls, L., Lewis, R. L., and Singh, S. (2022) · 2022
Earlier work this paper cites.
Multipl-e: A scalable and extensible approach to benchmarking neural code generation
Cassano, F., Gouwar, J., Nguyen, D., Nguyen, S., Phipps-Costin, L., Pinckney, D., Yee, M.-H., Zi, Y., Anderson, C. J., Feldman, M. Q., Guha, A., Greenberg, M., and Jangda, A. (2022) · 2022
Cited alongside, same era.
Codet: Code generation with generated tests
Chen, B., Zhang, F., Nguyen, A., Zan, D., Lin, Z., Lou, J.-G., and Chen, W. (2022) · 2022
Cited alongside, same era.
Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Le, H., Wang, Y., Gotmare, A. D., Savarese, S., and Hoi, S. C. H. (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.
Chain of thought prompting elicits reasoning in large language models
Dera: Enhancing large language model completions with dialog-enabled resolving agents
Nair, V., Schumacher, E., Tso, G., and Kannan, A. (2023) · 2023
Closest in time.
Gpt-4 technical report
OpenAI (2023) · 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) · 2023
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Refiner: Reasoning feedback on intermediate representations
Paul, D., Ismayilzada, M., Peyrard, M., Borges, B., Bosselut, A., West, R., and Faltings, B. (2023) · 2023
Closest in time.
Automatic prompt optimization with" gradient descent" and beam search
Pryzant, R., Iter, D., Li, J., Lee, Y. T., Zhu, C., and Zeng, M. (2023) · 2023
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D. (2022) · 2022
Cited alongside, same era.
Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D. (2023) · 2023
Cited alongside, same era.
Meta-prompt: A simple self-improving language agent
Goodman, N. (2023) · 2023
Cited alongside, same era.
Language models can solve computer tasks
Kim, G., Baldi, P., and McAleer, S. (2023) · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. (2023) · 2023
Cited alongside, same era.
Webshop: Towards scalable real-world web interaction with grounded language agents
Yao, S., Chen, H., Yang, J., and Narasimhan, K. (preprint)
Cited in the paper.
Closest in time.
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
Closest in time.
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
Closest in time.
Decomposition enhances reasoning via self-evaluation guided decoding
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, X., Kan, M.-Y., He, J., and Xie, Q. (2023) · 2023
Closest in time.
ReAct: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. (2023) · 2023
Closest in time.
Answering questions by meta-reasoning over multiple chains of thought
Yoran, O., Wolfson, T., Bogin, B., Katz, U., Deutch, D., and Berant, J. (2023) · 2023
Closest in time.