Effective Large Language Model Debugging with Best-first Tree Search
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929438916083712 |
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| author | Song, Jialin Raiman, Jonathan Catanzaro, Bryan |
| author_facet | Song, Jialin Raiman, Jonathan Catanzaro, Bryan |
| contents | Large Language Models (LLMs) show promise in code generation tasks. However, their code-writing abilities are often limited in scope: while they can successfully implement simple functions, they struggle with more complex tasks. A fundamental difference with how an LLM writes code, compared to a human programmer, is that it cannot consistently spot and fix bugs. Debugging is a crucial skill for programmers and it enables iterative code refinement towards a correct implementation. In this work, we propose a novel algorithm to enable LLMs to debug their code via self-reflection and search where a model attempts to identify its previous mistakes. Our key contributions are 1) a best-first tree search algorithm with self-reflections (BESTER) that achieves state-of-the-art Pass@1 in three code generation benchmarks. BESTER maintains its superiority when we measure pass rates taking into account additional inference costs incurred by tree search. 2) A novel interpretability study on what self-reflections attend to in buggy programs and how they impact bug fixes, which provides a deeper understanding of the debugging process. 3) An extensive study on when self-reflections are effective in finding bugs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Effective Large Language Model Debugging with Best-first Tree Search Song, Jialin Raiman, Jonathan Catanzaro, Bryan Software Engineering Artificial Intelligence Machine Learning Large Language Models (LLMs) show promise in code generation tasks. However, their code-writing abilities are often limited in scope: while they can successfully implement simple functions, they struggle with more complex tasks. A fundamental difference with how an LLM writes code, compared to a human programmer, is that it cannot consistently spot and fix bugs. Debugging is a crucial skill for programmers and it enables iterative code refinement towards a correct implementation. In this work, we propose a novel algorithm to enable LLMs to debug their code via self-reflection and search where a model attempts to identify its previous mistakes. Our key contributions are 1) a best-first tree search algorithm with self-reflections (BESTER) that achieves state-of-the-art Pass@1 in three code generation benchmarks. BESTER maintains its superiority when we measure pass rates taking into account additional inference costs incurred by tree search. 2) A novel interpretability study on what self-reflections attend to in buggy programs and how they impact bug fixes, which provides a deeper understanding of the debugging process. 3) An extensive study on when self-reflections are effective in finding bugs. |
| title | Effective Large Language Model Debugging with Best-first Tree Search |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.19055 |