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Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned., , , , , , , , , и 26 other автор(ы). CoRR, (2022)CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation., , , , , , , , , и 12 other автор(ы). NeurIPS Datasets and Benchmarks, (2021)Language Models (Mostly) Know What They Know, , , , , , , , , и 26 other автор(ы). (2022)cite arxiv:2207.05221Comment: 23+17 pages; refs added, typos fixed.Exploring and evaluating personalized models for code generation., , , , , и . ESEC/SIGSOFT FSE, стр. 1500-1508. ACM, (2022)The Capacity for Moral Self-Correction in Large Language Models., , , , , , , , , и 39 other автор(ы). CoRR, (2023)Language Models (Mostly) Know What They Know., , , , , , , , , и 26 other автор(ы). CoRR, (2022)Toy Models of Superposition., , , , , , , , , и 6 other автор(ы). CoRR, (2022)Generating bug-fixes using pretrained transformers., , , и . MAPS@PLDI, стр. 1-8. ACM, (2021)GraphCodeBERT: Pre-training Code Representations with Data Flow., , , , , , , , , и 8 other автор(ы). ICLR, OpenReview.net, (2021)Predictability and Surprise in Large Generative Models., , , , , , , , , и 20 other автор(ы). FAccT, стр. 1747-1764. ACM, (2022)