@inproceedings{huang-etal-2026-dont-know,
    title = "``{I} Don{'}t Know What to Say'': A Fact-Filling Questionnaire Method to Help Non-Experts Talk to {L}egal{AI} Assistant",
    author = "Huang, Yuting  and
      Wu, Yiquan  and
      Guo, Meitong  and
      Li, Ang  and
      Liu, Xiaozhong  and
      Yin, Keting  and
      Wu, Fei  and
      Kuang, Kun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.592/",
    pages = "12193--12210",
    ISBN = "979-8-89176-395-1",
    abstract = "Artificial intelligence has become increasingly prevalent in the legal domain. However, LegalAI systems often struggle with vague user queries that lack essential legal details, leading to suboptimal performance in practical applications. To address this challenge, we propose FactFiller, a novel approach that dynamically generates questionnaires to help users refine their input queries. Our method leverages an iterative training process that collects valuable questionnaires, eliminating the need for human annotation. Additionally, we introduce a ``case-law-quiz'' cascading retrieval process, ensuring that the generated questions and answer options are directly linked to specific legal provisions. Through the user study and the downstream task experiments, we demonstrate that FactFiller, while remaining easy for non-experts to understand, not only improves the completeness of queries but also ensures the performance of various domain-specific models in downstream legal tasks."
}