基于检索增强生成的多轮法律对话系统，提升法律问答准确性。
The 3rd Place Solution of CCIR CUP 2025: A Framework for Retrieval-Augmented Generation in Multi-Turn Legal Conversation
- 结合大模型与检索系统，从法律条文精准匹配答案。
- 在多轮对话中保持上下文连贯性，响应准确率显著提升。
- 适用于法律咨询、智能客服等专业场景。
检索增强生成技术在自然语言处理领域取得显著进展，通过融合信息检索与大语言模型的优势，能够基于可靠来源生成相关且符合语境的回复。该技术已在多个领域表现优异，但在法律领域的应用仍处于探索阶段。本文介绍我们在CCIR CUP 2025中针对“法律知识检索与生成”任务的解决方案，利用大语言模型与信息检索系统，根据用户问题从法律法规中提取相关信息并生成准确回答。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation has made significant progress in the field of natural language processing. By combining the advantages of information retrieval and large language models, RAG can generate relevant and contextually appropriate responses based on items retrieved from reliable sources. This technology has demonstrated outstanding performance across multiple domains, but its application in the legal field remains in its exploratory phase. In this paper, we introduce our approach for "Legal Knowledge Retrieval and Generation" in CCIR CUP 2025, which leverages large language models and information retrieval systems to provide responses based on laws in response to user questions.
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