Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
This addresses reliability issues in RAG systems for legal applications, offering a practical solution for handling large, structurally similar legal documents, though it is incremental as it builds on existing RAG methods.
The paper tackled the problem of unreliable retrieval in RAG systems for large legal datasets by identifying Document-Level Retrieval Mismatch (DRM) and proposing Summary-Augmented Chunking (SAC), which greatly reduces DRM and improves retrieval precision and recall in experiments.
Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically dependent on the accuracy of the retrieval step. This is particularly challenging in the legal domain, where large databases of structurally similar documents often cause retrieval systems to fail. In this paper, we address this challenge by first identifying and quantifying a critical failure mode we term Document-Level Retrieval Mismatch (DRM), where the retriever selects information from entirely incorrect source documents. To mitigate DRM, we investigate a simple and computationally efficient technique which we refer to as Summary-Augmented Chunking (SAC). This method enhances each text chunk with a document-level synthetic summary, thereby injecting crucial global context that would otherwise be lost during a standard chunking process. Our experiments on a diverse set of legal information retrieval tasks show that SAC greatly reduces DRM and, consequently, also improves text-level retrieval precision and recall. Interestingly, we find that a generic summarization strategy outperforms an approach that incorporates legal expert domain knowledge to target specific legal elements. Our work provides evidence that this practical, scalable, and easily integrable technique enhances the reliability of RAG systems when applied to large-scale legal document datasets.