Leveraging Print Debugging to Improve Code Generation in Large Language Models
This work addresses code generation challenges for developers using LLMs, but it is incremental as it builds on existing debugging methods.
The paper tackled the problem of suboptimal code generation in large language models for complex programming tasks by proposing an in-context learning approach using print debugging, resulting in performance improvements of 1.5% and 17.9% over rubber duck debugging on easy and medium-level Leetcode problems.
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 by using a "print debugging" method, which involves inserting print statements to trace and analysing logs for fixing the bug. We collect a Leetcode problem dataset and evaluate our method 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%.