Can Programming Languages Boost Each Other via Instruction Tuning?
Authors: name: Daoguang Zan, name: Ailun Yu, name: Bo Shen, name: Jiaxin Zhang, name: Taihong Chen, name: Bing Geng, name: Bei Chen, name: Jichuan Ji, name: Yafen Yao, name: Yongji Wang, name: Qianxiang Wang

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.