Can Programming Languages Boost Each Other via Instruction Tuning?

Daoguang Zan, Ailun Yu, Bo Shen, Jiaxin Zhang, Taihong Chen, Geng Bing, Bei Chen, Jichuan Ji, Yafen Yao, Wang, Qianxiang Wang

Can Programming Languages Boost Each Other via Instruction Tuning?: 12 upvotes on Hugging Face Daily Papers, #4 of 9 papers on 2023-09-01. Day-by-day upvote history.

When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models. We conduct extensive experiments of 8 popular programming languages (Python, JavaScript, TypeScript, C, C++, Java, Go, HTML) on StarCoder. Results demonstrate that programming languages can significantly improve each other. For example, CodeM-Python 15B trained on Python is able to increase Java by an absolute 17.95% pass@1 on HumanEval-X. More surprisingly, we found that CodeM-HTML 7B trained on the HTML corpus can improve Java by an absolute 15.24% pass@1. Our training data is released at https://github.com/NL2Code/CodeM.

Paper page on Hugging Face · arXiv

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