@inproceedings{zhang-etal-2025-llmtaxo,
    title = "{LLMT}axo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media",
    author = "Zhang, Haiqi  and
      Zhu, Zhengyuan  and
      Zhang, Zeyu  and
      Li, Chengkai",
    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/ingestion-acl-25/2025.findings-acl.1007/",
    pages = "19627--19641",
    ISBN = "979-8-89176-256-5",
    abstract = "With the rapid expansion of content on social media platforms, analyzing and comprehending online discourse has become increasingly complex. This paper introduces LLMTaxo, a novel framework leveraging large language models for the automated construction of taxonomies of factual claims from social media by generating topics at multiple levels of granularity. The resulting hierarchical structure significantly reduces redundancy and improves information accessibility. We also propose dedicated taxonomy evaluation metrics to enable comprehensive assessment. Evaluations conducted on three diverse datasets demonstrate LLMTaxo{'}s effectiveness in producing clear, coherent, and comprehensive taxonomies. Among the evaluated models, GPT-4o mini consistently outperforms others across most metrics. The framework{'}s flexibility and low reliance on manual intervention underscore its potential for broad applicability."
}