Code Needs Comments: Enhancing Code LLMs with Comment Augmentation

Demin Song; Honglin Guo; Yunhua Zhou; Shuhao Xing; Yudong Wang; Zifan Song; Wenwei Zhang; Qipeng Guo; Hang Yan; Xipeng Qiu; Dahua Lin

arXiv · 2402.13013

Code Needs Comments: Enhancing Code LLMs with Comment Augmentation

Demin Song·Honglin Guo·Yunhua Zhou·Shuhao Xing·Yudong Wang·Zifan Song·Wenwei Zhang·Qipeng Guo·Hang Yan·Xipeng Qiu·Dahua Lin

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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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BibTeX RIS

Demin Song, Honglin Guo, Yunhua Zhou, Shuhao Xing, Yudong Wang, Zifan Song, Wenwei Zhang, Qipeng Guo, Hang Yan, Xipeng Qiu, Dahua Lin. 2024-02-20. Code Needs Comments: Enhancing Code LLMs with Comment Augmentation. https://arxiv.org/abs/2402.13013

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