@inproceedings{zeng-etal-2025-codetaxo,
    title = "{C}ode{T}axo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts",
    author = "Zeng, Qingkai  and
      Bai, Yuyang  and
      Tan, Zhaoxuan  and
      Wu, Zhenyu  and
      Feng, Shangbin  and
      Jiang, Meng",
    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/corrections-2025-08/2025.findings-acl.214/",
    doi = "10.18653/v1/2025.findings-acl.214",
    pages = "4131--4144",
    ISBN = "979-8-89176-256-5",
    abstract = "Taxonomies provide structural representations of knowledge and are crucial in various applications. The task of taxonomy expansion involves integrating emerging entities into existing taxonomies by identifying appropriate parent entities for these new query entities. Previous methods rely on self-supervised techniques that generate annotation data from existing taxonomies but are less effective with small taxonomies (fewer than 100 entities). In this work, we introduce CodeTaxo, a novel approach that leverages large language models through code language prompts to capture the taxonomic structure. Extensive experiments on five real-world benchmarks from different domains demonstrate that CodeTaxo consistently achieves superior performance across all evaluation metrics, significantly outperforming previous state-of-the-art methods. The code and data are available at \url{https://github.com/QingkaiZeng/CodeTaxo-official}."
}