Leveraging Print Debugging to Improve Code Generation in Large Language Models
Keywords: Large Language Model, In-context Learning, Code Generation
Abstract: Large language models (LLMs) have made significant progress in code generation tasks, but their performance in tackling programming problems with complex data structures and algorithms remains suboptimal. To address this issue, we propose an in-context learning approach that guides LLMs to debug using a ``print debugging'' strategy, which involves inserting print statements to trace and analysing logs for fixing the bug. We collect a Leetcode problem dataset and evaluate our methodology using the Leetcode online judging system. Experiments with GPT-4 demonstrate the effectiveness of our approach, outperforming rubber duck debugging in easy and medium-level Leetcode problems by 1.5\% and 17.9\%.
Primary Area: generative models
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Submission Number: 7491
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