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.
Can Programming Languages Boost Each Other via Instruction Tuning?
Programming languages enhance each other during the instruction fine-tuning phase of code large language models, as demonstrated by significant improvements in test performance across different languages.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 11
- Hosting
- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/2308.16824v2ARXIV-DEFAULT
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- Semantic Scholar