Reducing hallucinations in large language models through retrieval-augmented generation (RAG)

Abstract

With the growing need for precise, context-aware information retrieval (IR) tools in the legal domain, this study explores the potential of Retrieval-Augmented Generation (RAG) for retrieving and synthesizing legal content. Leveraging a dataset of legal documents spanning contract law and privacy policies, we implement and evaluate the performance of various RAG configurations. By benchmarking each approach on criteria such as retrieval accuracy and contextual relevance, we identify solutions suited for professional deployment. Our results provide insight into how RAG addresses key challenges in legal information retrieval, such as mitigating hallucinations and improving the quality of results. By exploring different RAG techniques and their impact on performance, this research provides a pathway for integrating AI-driven solutions into professional legal environments.