@inproceedings{sun-etal-2025-two,
    title = "A Two-Stage {LLM} System for Enhanced Regulatory Information Retrieval and Answer Generation",
    author = "Sun, Fengzhao  and
      Yu, Jun  and
      Hou, Jiaming  and
      Lin, Yutong  and
      Liu, Tianyu",
    editor = "Gokhan, Tuba  and
      Wang, Kexin  and
      Gurevych, Iryna  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.regnlp-1.10/",
    pages = "68--72",
    abstract = "This technical report describes our methodology for the Regulatory Information Retrieval and Answer Generation (RIRAG) Shared Task, a component of the RegNLP workshop at COLING 2025. The challenge aims to effectively navigate and extract relevant information from regulatory texts to generate precise, coherent answers for compliance and obligation-related queries. To tackle subtask1, we introduce a two-stage approach comprising an initial output stage and a subsequent refinement stage. Initially, we fine-tune the LLaMa-2-7B model using LoRA to produce a preliminary output. This is followed by the application of an expert mechanism to enhance the results. For subtask2, we design specific prompt to facilitate the generation of high-quality answers. Consequently, our approach has achieved state-of-the-art performance on the leaderboard, which serves as a testament to the effectiveness and competitiveness of our proposed methodology."
}