@inproceedings{trainin-abend-2025-t5score,
    title = "$T^5Score$: A Methodology for Automatically Assessing the Quality of {LLM} Generated Multi-Document Topic Sets",
    author = "Trainin, Itamar  and
      Abend, Omri",
    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/display_plenaries/2025.findings-acl.1351/",
    pages = "26347--26375",
    ISBN = "979-8-89176-256-5",
    abstract = "Using LLMs for Multi-Document Topic Extraction has recently gained popularity due to their apparent high-quality outputs, expressiveness, and ease of use. However, most existing evaluation practices are not designed for LLM-generated topics and result in low inter-annotator agreement scores, hindering the reliable use of LLMs for the task. To address this, we introduce $T^5Score$, an evaluation methodology that decomposes the quality of a topic set into quantifiable aspects, measurable through easy-to-perform annotation tasks. This framing enables a convenient, manual or automatic, evaluation procedure resulting in a strong inter-annotator agreement score.To substantiate our methodology and claims, we perform extensive experimentation on multiple datasets and report the results."
}