@inproceedings{li-etal-2025-mpl,
    title = "{MPL}: Multiple Programming Languages with Large Language Models for Information Extraction",
    author = "Li, Bo  and
      Fang, Gexiang  and
      Ye, Wei  and
      Xu, Zhenghua  and
      Zhang, Jinglei  and
      Cheng, Hao  and
      Zhang, Shikun",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.122/",
    doi = "10.18653/v1/2025.findings-acl.122",
    pages = "2403--2414",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent research in information extraction (IE) focuses on utilizing code-style inputs to enhance structured output generation. The intuition behind this is that the programming languages (PLs) inherently exhibit greater structural organization than natural languages (NLs). This structural advantage makes PLs particularly suited for IE tasks. Nevertheless, existing research primarily focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs (e.g., C++ and Java) during the supervised fine-tuning (SFT) phase. In this research, we propose Multiple Programming Languages with large language models for information extraction (abbreviated as MPL), a novel framework that explores the potential of incorporating different PLs in the SFT phase. Additionally, we introduce function-prompt with virtual running to simulate code-style inputs more effectively and efficiently. Experimental results on a wide range of datasets demonstrate the effectiveness of MPL. Furthermore, we conduct extensive experiments to provide a comprehensive analysis. Our code and additional files are in the supplementary materials."
}