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Code Needs Comments: Enhancing Code LLMs with Comment Augmentation
Feb. 21, 2024, 5:48 a.m. | Demin Song, Honglin Guo, Yunhua Zhou, Shuhao Xing, Yudong Wang, Zifan Song, Wenwei Zhang, Qipeng Guo, Hang Yan, Xipeng Qiu, Dahua Lin
cs.CL updates on arXiv.org arxiv.org
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 …
abstract arxiv augmentation code code llms correlation cs.cl data impact language language models languages large language large language models llms natural performance pre-training programming programming languages training training data type understanding
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