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The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G · 1909
Earlier work this paper cites.
Toward automatic program synthesis
Manna, Z. and Waldinger, R. J · 1971
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Teaching machines to describe images with natural language feedback
Fidler, S. et al · 2017
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e-snli: Natural language inference with natural language explanations
Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., and Blunsom, P · 2018
Earlier work this paper cites.
Spoc: Search-based pseudocode to code
Kulal, S., Pasupat, P., Chandra, K., Lee, M., Padon, O., Aiken, A., and Liang, P. S · 2019
Earlier work this paper cites.
A survey of reinforcement learning informed by natural language
Luketina, J., Nardelli, N., Farquhar, G., Foerster, J., Andreas, J., Grefenstette, E., Whiteson, S., and Rocktäschel, T · 2019
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Earlier work this paper cites.
Speak to your parser: Interactive text-to-SQL with natural language feedback
Elgohary, A., Hosseini, S., and Hassan Awadallah, A · 2020
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CodeBERT: A pre-trained model for programming and natural languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., and Zhou, M · 2020
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The Pile: An 800gb dataset of diverse text for language modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C · 2020
Earlier work this paper cites.
A review on interactive reinforcement learning from human social feedback
Lin, J., Ma, Z., Gomez, R., Nakamura, K., He, B., and Li, G · 2020
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Learning to summarize with human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
Earlier work this paper cites.
Program synthesis with large language models, 2021
Austin, J., Odena, A., Nye, M. I., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C. J., Terry, M., Le, Q. V., and Sutton, C · 2021
Cited alongside, same era.
Evaluating large language models trained on code, 2021
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., and Zaremba, W · 2021
Cited alongside, same era.
Hase, P. and Bansal, M · 2021
Cited alongside, same era.
Datasets: A community library for natural language processing
Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., Hubert, T., Choy, P., de Masson d’Autume, C., Babuschkin, I., Chen, X., Huang, P.-S., Welbl, J., Gowal, S., Cherepanov, A., Molloy, J., Mankowitz, D. J., Robson, E. S., Kohli, P., de Freitas, N., Kavukcuoglu, K., and Vinyals, O · 2022
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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
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Model index for researchers, 2022
OpenAI · 2022
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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., Gray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
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Training language models with language feedback
Scheurer, J., Campos, J. A., Chan, J. S., Chen, A., Cho, K., and Perez, E · 2022
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Lhoest, Q., Villanova del Moral, A., Jernite, Y., Thakur, A., von Platen, P., Patil, S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., Davison, J., Šaško, M., Chhablani, G., Malik, B., Brandeis, S., Le Scao, T., Sanh, V., Xu, C., Patry, N., McMillan-Major, A., Schmid, P., Gugger, S., Delangue, C., Matussière, T., Debut, L., Bekman, S., Cistac, P., Goehringer, T., Mustar, V., Lagunas, F., Rush, A., and Wolf, T · 2021
Cited alongside, same era.
Program synthesis with large language models
Odena, A., Sutton, C., Dohan, D. M., Jiang, E., Michalewski, H., Austin, J., Bosma, M. P., Nye, M., Terry, M., and Le, Q. V · 2021
Cited alongside, same era.
Evaluating Explanations: How much do explanations from the teacher aid students?, 2021
Pruthi, D., Bansal, R., Dhingra, B., Soares, L. B., Collins, M., Lipton, Z. C., Neubig, G., and Cohen, W. W · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B. and Komatsuzaki, A · 2021
Cited alongside, same era.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Wang, Y., Wang, W., Joty, S., and Hoi, S. C · 2021
Cited alongside, same era.
GPT-NeoX-20B: An open-source autoregressive language model
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., Pieler, M., Prashanth, U. S., Purohit, S., Reynolds, L., Tow, J., Wang, B., and Weinbach, S · 2022
Cited alongside, same era.
Measuring progress on scalable oversight for large language models
Bowman, S. R., Hyun, J., Perez, E., Chen, E., Pettit, C., Heiner, S., Lukošiūtė, K., Askell, A., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Olah, C., Amodei, D., Amodei, D., Drain, D., Li, D., Tran-Johnson, E., Kernion, J., Kerr, J., Mueller, J., Ladish, J., Landau, J., Ndousse, K., Lovitt, L., Elhage, N., Schiefer, N., Joseph, N., Mercado, N., DasSarma, N., Larson, R., McCandlish, S., Kundu, S., Johnston, S., Kravec, S., Showk, S. E., Fort, S., Telleen-Lawton, T., Brown, T., Henighan, T., Hume, T., Bai, Y., Hatfield-Dodds, Z., Mann, B., and Kaplan, J · 2022
Cited alongside, same era.
Large language models are few-shot testers: Exploring llm-based general bug reproduction, 2022
Kang, S., Yoon, J., and Yoo, S · 2022
Cited alongside, same era.
Can language models learn from explanations in context?
Lampinen, A. K., Dasgupta, I., Chan, S. C., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., and Hill, F · 2022
Cited alongside, same era.
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Solving math word problems with process- and outcome-based feedback, 2022
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., brian ichter, Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2022
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Bloom: A 176b-parameter open-access multilingual language model, 2022
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A., Nezhurina, M., Sänger, M., Samwald, M., Cullan, M., Weinberg, M., De Wolf, M., Mihaljcic, M., Liu, M., Freidank, M., Kang, M., Seelam, N., Dahlberg, N., Broad, N. M., Muellner, N., Fung, P., Haller, P., Chandrasekhar, R., Eisenberg, R., Martin, R., Canalli, R., Su, R., Su, R., Cahyawijaya, S., Garda, S., Deshmukh, S. S., Mishra, S., Kiblawi, S., Ott, S., Sang-aroonsiri, S., Kumar, S., Schweter, S., Bharati, S., Laud, T., Gigant, T., Kainuma, T., Kusa, W., Labrak, Y., Bajaj, Y. S., Venkatraman, Y., Xu, Y., Xu, Y., Xu, Y., Tan, Z., Xie, Z., Ye, Z., Bras, M., Belkada, Y., and Wolf, T · 2022
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A systematic evaluation of large language models of code
Xu, F. F., Alon, U., Neubig, G., and Hellendoorn, V. J · 2022
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CERT: Continual pre-training on sketches for library-oriented code generation
Zan, D., Chen, B., Yang, D., Lin, Z., Kim, M., Guan, B., Wang, Y., Chen, W., and Lou, J.-G · 2022
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Productivity assessment of neural code completion
Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin, D., Simister, S., Sittampalam, G., and Aftandilian, E · 2022
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Training language models with language feedback at scale
Scheurer, J., Campos, J. A., Korbak, T., Chan, J. S., Chen, A., Cho, K., and Perez, E · 2023
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