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Predictability and Surprise in Large Generative Models., , , , , , , , , and 20 other author(s). FAccT, page 1747-1764. ACM, (2022)Evaluating Large Language Models Trained on Code., , , , , , , , , and 48 other author(s). CoRR, (2021)Evaluating and Mitigating Discrimination in Language Model Decisions., , , , , , , , and . CoRR, (2023)Discovering Language Model Behaviors with Model-Written Evaluations., , , , , , , , , and 53 other author(s). CoRR, (2022)Constitutional AI: Harmlessness from AI Feedback., , , , , , , , , and 41 other author(s). CoRR, (2022)Towards Measuring the Representation of Subjective Global Opinions in Language Models., , , , , , , , , and 8 other author(s). CoRR, (2023)Predictability and Surprise in Large Generative Models., , , , , , , , , and 20 other author(s). CoRR, (2022)Measuring Faithfulness in Chain-of-Thought Reasoning., , , , , , , , , and 20 other author(s). CoRR, (2023)Question Decomposition Improves the Faithfulness of Model-Generated Reasoning., , , , , , , , , and 14 other author(s). CoRR, (2023)Specific versus General Principles for Constitutional AI., , , , , , , , , and 26 other author(s). CoRR, (2023)