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Language Models (Mostly) Know What They Know, , , , , , , , , and 26 other author(s). (2022)cite arxiv:2207.05221Comment: 23+17 pages; refs added, typos fixed.Language Models (Mostly) Know What They Know., , , , , , , , , and 26 other author(s). CoRR, (2022)Measuring Progress on Scalable Oversight for Large Language Models., , , , , , , , , and 36 other author(s). CoRR, (2022)Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback., , , , , , , , , and 21 other author(s). CoRR, (2022)The Capacity for Moral Self-Correction in Large Language Models., , , , , , , , , and 39 other author(s). CoRR, (2023)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, , , , , , , , , and 441 other author(s). (2022)cite arxiv:2206.04615Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench.Predictability and Surprise in Large Generative Models., , , , , , , , , and 20 other author(s). CoRR, (2022)Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training., , , , , , , , , and 29 other author(s). CoRR, (2024)A General Language Assistant as a Laboratory for Alignment., , , , , , , , , and 12 other author(s). CoRR, (2021)Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned., , , , , , , , , and 26 other author(s). CoRR, (2022)