针对印度最高法院判例，提升法律问答的上下文相关性。
Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments
- 按修辞角色切分法律文本，增强检索精准度。
- 融合对话历史与查询重写，理解用户连续提问意图。
- 适配法律文书结构，适合法律AI研究与实务应用。
本研究提出一种面向印度最高法院判例的法律领域专用检索增强生成（RAG）框架，旨在支持交互式检索与判例推理。该框架通过基于修辞角色的文本切块、融合式检索及交叉编码器重排序方法，显著提升所获信息的相关性。为改善对话体验，系统结合聊天历史、查询分类与重写技术，以更好理解用户连续提问意图。同时，引入对法律文档结构特征的考量，如法官姓名独立识别，进一步优化检索质量。在DeepEval框架下的评估显示，该框架在上下文召回率和答案相关性等指标上表现优异，验证了其在需大量上下文支持的法律问答任务中的有效性。结果强调了领域特定增强对构建可靠且可解释的法律AI系统的重要性。
原文摘要 · Abstract (English)
This research paper proposes a Retrieval Augmented Generation (RAG) framework that is specific to the legal field in order to assist interactive retrieval and reason about judgments from the Supreme Court of India. The solution uses an enhanced version of RAG framework which consists of rhetorically based chunking, fusion-based retrieval, and cross encoder reranking methods to increase the relevancy of the information retrieved. In order to improve conversations, the proposed framework uses chat history along with query classification and rewriting in order to understand user intention from successive queries. Additionally, there are features that take into account structural aspects of legal documents, such as isolated names of judges that could have an impact on retrieval quality. The evaluation was done using the DeepEval framework and demonstrated strong performance on metrics including contextual recall and answer relevancy, which proves that the framework is very effective in dealing with legal question-answering tasks that require a lot of context. The results emphasize the importance of domain specific enhancements in developing legal AI systems that are both reliable and explainable.
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