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Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned., , , , , , , , , and 26 other author(s). CoRR, (2022)CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation., , , , , , , , , and 12 other author(s). NeurIPS Datasets and Benchmarks, (2021)Toy Models of Superposition., , , , , , , , , and 6 other author(s). CoRR, (2022)Generating bug-fixes using pretrained transformers., , , and . MAPS@PLDI, page 1-8. ACM, (2021)Predictability and Surprise in Large Generative Models., , , , , , , , , and 20 other author(s). FAccT, page 1747-1764. ACM, (2022)GraphCodeBERT: Pre-training Code Representations with Data Flow., , , , , , , , , and 8 other author(s). ICLR, OpenReview.net, (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)Exploring and evaluating personalized models for code generation., , , , , and . ESEC/SIGSOFT FSE, page 1500-1508. ACM, (2022)Language Models (Mostly) Know What They Know., , , , , , , , , and 26 other author(s). CoRR, (2022)