Leveraging Print Debugging to Improve Code Generation in Large Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911754124001280 |
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| author | Hu, Xueyu Kuang, Kun Sun, Jiankai Yang, Hongxia Wu, Fei |
| author_facet | Hu, Xueyu Kuang, Kun Sun, Jiankai Yang, Hongxia Wu, Fei |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05319 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Print Debugging to Improve Code Generation in Large Language Models Hu, Xueyu Kuang, Kun Sun, Jiankai Yang, Hongxia Wu, Fei Computation and Language Software Engineering 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%. |
| title | Leveraging Print Debugging to Improve Code Generation in Large Language Models |
| topic | Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2401.05319 |