@inproceedings{seo-etal-2025-fq,
    title = "{FQ}-Eval: Building Evaluation Dataset for User-centered Follow-up Question Generation",
    author = "Seo, Sanghyun  and
      Kang, Bumsoo  and
      Lee, Dahm  and
      Kim, Jaeheon  and
      Shin, Joongbo  and
      Kim, Eui Soon  and
      Jeon, Kijeong",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.188/",
    pages = "2811--2827",
    ISBN = "979-8-89176-333-3",
    abstract = "To effectively support users' goal achievement in chat-LLM services, providing user-centered follow-up questions is essential. Existing studies primarily focus on enhancing information-seeking or topical relevance, often missing how follow-up questions could satisfy users' intrinsic needs and conversational goals. To bridge this gap, we introduce FQ-Eval, a user-centered evaluation dataset designed for assessing follow-up question generation in chat-LLM services. FQ-Eval incorporates realistic chat-LLM usage scenarios and five distinct human-aligned criteria, each reflecting user expectations of effective follow-up questions. Experimental results show that FQ-Eval constructed through our approach clearly capture human-aligned criteria, enabling robust, human-aligned follow-up question generation evaluation of various models and services."
}