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Computer Science > Computation and Language

arXiv:2304.05128 (cs)
[Submitted on 11 Apr 2023]

Title:Teaching Large Language Models to Self-Debug

Authors:Xinyun Chen, Maxwell Lin, Nathanael Scharli, Denny Zhou
Download a PDF of the paper titled Teaching Large Language Models to
Self-Debug, by Xinyun Chen and 3 other authors
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    Abstract: Large language models (LLMs) have achieved impressive
    performance on code generation. However, for complex programming
    tasks, generating the correct solution in one go becomes
    challenging, thus some prior works have designed program repair
    approaches to improve code generation performance. In this work,
    we propose Self-Debugging, which teaches a large language model
    to debug its predicted program via few-shot demonstrations. In
    particular, we demonstrate that Self-Debugging can teach the
    large language model to perform rubber duck debugging; i.e.,
    without any feedback on the code correctness or error messages,
    the model is able to identify its mistakes by explaining the
    generated code in natural language. Self-Debugging achieves the
    state-of-the-art performance on several code generation
    benchmarks, including the Spider dataset for text-to-SQL
    generation, TransCoder for C++-to-Python translation, and MBPP
    for text-to-Python generation. On the Spider benchmark where
    there are no unit tests to verify the correctness of predictions,
    Self-Debugging with code explanation consistently improves the
    baseline by 2-3%, and improves the prediction accuracy on
    problems of the hardest label by 9%. On TransCoder and MBPP where
    unit tests are available, Self-Debugging improves the baseline
    accuracy by up to 12%. Meanwhile, by leveraging feedback messages
    and reusing failed predictions, Self-Debugging notably improves
    sample efficiency, and can match or outperform baseline models
    that generate more than 10x candidate programs.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2304.05128 [cs.CL]
          (or arXiv:2304.05128v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2304.05128
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Xinyun Chen [view email]
[v1] Tue, 11 Apr 2023 10:43:43 UTC (350 KB)
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