@inproceedings{li-etal-2025-drs,
    title = "{DRS}: Deep Question Reformulation With Structured Output",
    author = "Li, Zhecheng  and
      Wang, Yiwei  and
      Hooi, Bryan  and
      Cai, Yujun  and
      Peng, Nanyun  and
      Chang, Kai-Wei",
    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/landing_page/2025.findings-acl.666/",
    pages = "12869--12882",
    ISBN = "979-8-89176-256-5",
    abstract = "Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions that the text itself cannot answer due to insufficient understanding of the underlying information. Recent studies reveal that while LLMs can detect unanswerable questions, they struggle to assist users in reformulating these questions. Even advanced models like GPT-3.5 demonstrate limited effectiveness in this regard. To address this limitation, we propose DRS: Deep Question Reformulation with Structured Output, a novel zero-shot method aimed at enhancing LLMs' ability to assist users in reformulating questions to extract relevant information from new documents. DRS combines the strengths of LLMs with a DFS-based algorithm to iteratively explore potential entity combinations and constrain outputs using predefined entities. This structured approach significantly enhances the reformulation capabilities of LLMs. Comprehensive experimental evaluations demonstrate that DRS improves the reformulation accuracy of GPT-3.5 from 23.03{\%} to 70.42{\%}, while also enhancing the performance of open-source models, such as Gemma2-9B, from 26.35{\%} to 56.75{\%}."
}