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arXiv · 2308.16824

Can Programming Languages Boost Each Other via Instruction Tuning?

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

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.

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Daoguang Zan, Ailun Yu, Bo Shen, Jiaxin Zhang, Taihong Chen, Bing Geng, Bei Chen, Jichuan Ji, Yafen Yao, Yongji Wang, Qianxiang Wang. 2023-08-31. Can Programming Languages Boost Each Other via Instruction Tuning?. https://arxiv.org/abs/2308.16824

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