Enhancing the Precision and Interpretability of Retrieval-Augmented Generation (RAG) in Legal Technology: A Survey
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Abstract
Retrieval-Augmented Generation (RAG) is a promising solution that can enhance the capabilities of large language model (LLM) applications in critical domains, including legal technology, by retrieving knowledge from external databases. Implementing RAG pipelines requires careful attention to the techniques and methods implemented in the different stages of the RAG process. However, robust RAG can enhance LLM generation with faithfulness and few hallucinations in responses. In this paper, we discuss the application of RAG in the legal domain. First, we present an overview of the main RAG methods, stages, techniques, and applications in the legal domain. We then briefly discuss the different information retrieval models, processes, and applied methods in current legal RAG solutions. Then, we explain the different quantitative and qualitative evaluation metrics. We also describe several emerging datasets and benchmarks. We then discuss and assess the ethical and privacy considerations for legal RAG and summarize various challenges, and propose a challenge scale based on RAG failure points and control over external knowledge. Finally, we provide insights into promising future research to leverage RAG efficiently and effectively in the legal field.