Code Needs Comments: Enhancing Code Llms With Comment Augmentation
Song Demin, Guo Honglin, Zhou Yunhua, Xing Shuhao, Wang Yudong, Song Zifan, Zhang Wenwei, Guo Qipeng, Yan Hang, Qiu Xipeng, Lin Dahua. Arxiv 2024
[Paper]
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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