title: Can Programming Languages Boost Each Other via Instruction Tuning?
summary: 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_authors._id: 64f145f753c3b575c28114d5
paper_authors.name: Daoguang Zan
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paper_authors.name: Bo Shen
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paper_authors.name: Jiaxin Zhang
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paper_authors.name: Taihong Chen
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paper_authors.name: Bing Geng
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paper_authors.fullname: Geng Bing
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paper_authors.user: edwingeng
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paper_authors.name: Bei Chen
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paper_authors.fullname: Bei Chen
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paper_authors.user: BellaBei
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paper_authors.name: Jichuan Ji
paper_authors._id: 64f145f753c3b575c28114dd
paper_authors.name: Yafen Yao
paper_authors._id: 64f145f753c3b575c28114de
paper_authors.name: Yongji Wang
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paper_authors.fullname: Wang
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paper_authors.user: Yongji
paper_authors._id: 64f145f753c3b575c28114df
paper_authors.name: Qianxiang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-01T12:30:51.892Z
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paper_authors.avatarUrl: /avatars/6c7b096d495d1128e19d4fb18789e129.svg
paper_authors.fullname: Qianxiang Wang
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paper_authors.user: WangQ
paper_title: Can Programming Languages Boost Each Other via Instruction Tuning?
paper_summary: 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_ai_keywords: instruction StarCoder CodeM-Python pass@1 HumanEval-X CodeM-HTML