利用LLMs的推理能力推断法律信息检索中的隐含概念
Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval
AI总结：
本文提出利用LLMs的逻辑推理能力识别查询中隐含的法律术语与事实，通过术语扩展和查询重构增强检索系统，在COLIEE数据集上超越了所有参赛队伍的最高成绩。
摘要：
成文法检索是法律语言处理中的典型问题，在法律工程中有诸多实际应用。现代基于深度学习的检索方法在该问题上取得了显著成果。然而，依赖语义和词汇相关性的检索系统往往存在局限性，特别是在处理涉及现实生活场景的查询，或使用非法律领域特定词汇时。在本研究中，我们致力于通过利用大型语言模型（LLMs）的逻辑推理能力来克服这一弱点，以识别与查询中提及情况相关的法律术语和事实。所提出的检索系统整合了来自基于术语扩展和查询重构的额外信息，从而提高检索准确性。在COLIEE 2022和COLIEE 2023数据集上的实验表明，来自LLMs的额外知识有助于提升词汇和语义排序模型的检索结果。最终的集成检索系统超越了COLIEE 2022和2023竞赛中所有参赛队伍的最高成绩。
英文摘要：
Statutory law retrieval is a typical problem in legal language processing, that has various practical applications in law engineering. Modern deep learning-based retrieval methods have achieved significant results for this problem. However, retrieval systems relying on semantic and lexical correlations often exhibit limitations, particularly when handling queries that involve real-life scenarios, or use the vocabulary that is not specific to the legal domain. In this work, we focus on overcoming this weaknesses by utilizing the logical reasoning capabilities of large language models (LLMs) to identify relevant legal terms and facts related to the situation mentioned in the query. The proposed retrieval system integrates additional information from the term--based expansion and query reformulation to improve the retrieval accuracy. The experiments on COLIEE 2022 and COLIEE 2023 datasets show that extra knowledge from LLMs helps to improve the retrieval result of both lexical and semantic ranking models. The final ensemble retrieval system outperformed the highest results among all participating teams in the COLIEE 2022 and 2023 competitions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
