Code Needs Comments: Enhancing Code LLMs with Comment Augmentation

• URL: http://arxiv.org/abs/2402.13013v1
• Date: Tue, 20 Feb 2024 13:56:38 GMT
• Title: Code Needs Comments: Enhancing Code LLMs with Comment Augmentation
• Authors: Demin Song, Honglin Guo, Yunhua Zhou, Shuhao Xing, Yudong Wang, Zifan Song, Wenwei Zhang, Qipeng Guo, Hang Yan, Xipeng Qiu, Dahua Lin
• Abstract summary: We introduce a novel data augmentation method that generates comments for existing code, coupled with a data filtering strategy that filters out code data poorly correlated with natural language. We conducted experiments on three code-focused Large Language Models and observed consistent improvements in performance on two widely-used programming skill benchmarks.
• Score: 91.52444946362547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The programming skill is one crucial ability for Large Language Models (LLMs), necessitating a deep understanding of programming languages (PLs) and their correlation with natural languages (NLs). We examine the impact of pre-training data on code-focused LLMs' performance by assessing the comment density as a measure of PL-NL alignment. Given the scarcity of code-comment aligned data in pre-training corpora, we introduce a novel data augmentation method that generates comments for existing code, coupled with a data filtering strategy that filters out code data poorly correlated with natural language. We conducted experiments on three code-focused LLMs and observed consistent improvements in performance on two widely-used programming skill benchmarks. Notably, the model trained on the augmented data outperformed both the model used for generating comments and the model further trained on the data without augmentation.

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