Jiaming Hou


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2025

pdf bib
A Two-Stage LLM System for Enhanced Regulatory Information Retrieval and Answer Generation
Fengzhao Sun | Jun Yu | Jiaming Hou | Yutong Lin | Tianyu Liu
Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)

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.