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This paper introduces a Legal Question Answering (LQA) dataset, consisting of 10,000 annotated legal question-answer pairs in Chinese. We build the legal question-answer system on LQA datasets by implementing the “retrieve-then-read” pipeline, which could offer answers grounded in pertinent legal statutes. The experimental results have validated the efficacy of the data set in enhancing the performance of the legal question-answer system. The constructed LQA datasets could be used to train and refine LQA systems to better understand and respond to legal questions, thus enhancing the capabilities of AI in the legal domain.

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Research on Legal Question Answering System with Retrieval-Augmented Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Nuo Xu,
  • Siben Li,
  • Yufan Xia

摘要

This paper introduces a Legal Question Answering (LQA) dataset, consisting of 10,000 annotated legal question-answer pairs in Chinese. We build the legal question-answer system on LQA datasets by implementing the “retrieve-then-read” pipeline, which could offer answers grounded in pertinent legal statutes. The experimental results have validated the efficacy of the data set in enhancing the performance of the legal question-answer system. The constructed LQA datasets could be used to train and refine LQA systems to better understand and respond to legal questions, thus enhancing the capabilities of AI in the legal domain.