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Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned., , , , , , , , , and 26 other author(s). CoRR, (2022)Towards Understanding Sycophancy in Language Models., , , , , , , , , and 8 other author(s). ICLR, OpenReview.net, (2024)Predictability and Surprise in Large Generative Models., , , , , , , , , and 20 other author(s). FAccT, page 1747-1764. ACM, (2022)Language Models are Few-Shot Learners., , , , , , , , , and 21 other author(s). NeurIPS, (2020)Learning Transferable Visual Models From Natural Language Supervision, , , , , , , , , and 2 other author(s). (2021)Language Models (Mostly) Know What They Know, , , , , , , , , and 26 other author(s). (2022)cite arxiv:2207.05221Comment: 23+17 pages; refs added, typos fixed.The Capacity for Moral Self-Correction in Large Language Models., , , , , , , , , and 39 other author(s). CoRR, (2023)Language Models (Mostly) Know What They Know., , , , , , , , , and 26 other author(s). CoRR, (2022)Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims., , , , , , , , , and 49 other author(s). CoRR, (2020)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models., , , , , , , , , and 440 other author(s). Trans. Mach. Learn. Res., (2023)