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Few-shot Adaptation Works with UnpredicTable Data.

, , , , and . ACL (1), page 1806-1842. Association for Computational Linguistics, (2023)

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Few-shot Adaptation Works with UnpredicTable Data., , , , and . CoRR, (2022)Retrospective on the 2021 BASALT Competition on Learning from Human Feedback., , , , , , , , , and 6 other author(s). CoRR, (2022)Learning from Natural Language Feedback., , , , , and . CoRR, (2022)Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark., , , , , , , , , and . CoRR, (2023)Training Language Models with Language Feedback at Scale., , , , , , and . CoRR, (2023)Few-shot Adaptation Works with UnpredicTable Data., , , , and . ACL (1), page 1806-1842. Association for Computational Linguistics, (2023)Improving Code Generation by Training with Natural Language Feedback., , , , , , , and . CoRR, (2023)How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios., , , , , , , , and . NeurIPS, (2022)Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback., , , , , , , , , and 6 other author(s). NeurIPS (Competition and Demos), volume 176 of Proceedings of Machine Learning Research, page 259-272. PMLR, (2021)Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark., , , , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 26837-26867. PMLR, (2023)