GuRE:Generative Query REwriter for Legal Passage Retrieval

• URL: http://arxiv.org/abs/2505.12950v1
• Date: Mon, 19 May 2025 10:42:36 GMT
• Title: GuRE:Generative Query REwriter for Legal Passage Retrieval
• Authors: Daehee Kim, Deokhyung Kang, Jonghwi Kim, Sangwon Ryu, Gary Geunbae Lee,
• Abstract summary: Legal Passage Retrieval (LPR) systems are crucial as they help practitioners save time when drafting legal arguments.<n>One primary reason is the significant vocabulary mismatch between the query and the target passage.<n>We propose a simple yet effective method, the Generative query REwriter (GuRE)
• Score: 4.170994234169557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Legal Passage Retrieval (LPR) systems are crucial as they help practitioners save time when drafting legal arguments. However, it remains an underexplored avenue. One primary reason is the significant vocabulary mismatch between the query and the target passage. To address this, we propose a simple yet effective method, the Generative query REwriter (GuRE). We leverage the generative capabilities of Large Language Models (LLMs) by training the LLM for query rewriting. "Rewritten queries" help retrievers to retrieve target passages by mitigating vocabulary mismatch. Experimental results show that GuRE significantly improves performance in a retriever-agnostic manner, outperforming all baseline methods. Further analysis reveals that different training objectives lead to distinct retrieval behaviors, making GuRE more suitable than direct retriever fine-tuning for real-world applications. Codes are avaiable at github.com/daehuikim/GuRE.

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