GraphRAG Makes it Possible to Digest Convoluted Legal Jargon

Legal jargon easily overwhelms those who lack proper training. Aspiring entrepreneurs may feel intimidated by the complex web of regulations between them and their goals. Even when available, crucial information can be extensive and difficult to navigate without costly professional help. This article leverages recent advancements in the field of Natural Language Processing (NLP) to investigate the potential of Large Language Models (LLMs) in helping stakeholders understand the legal requirements of business aspirations. Inspired by [7], we compare 2 Machine Learning (ML) retrieval methods-Retrieval Augmented Generation (RAG), and Graph Retrieval Augmented Generation (GraphRAG)-assessing their performance on 2 sets of LLM generated queries. More specifically, our study evaluates 6 sub-models-4 from Light Retrieval Augmented Generation (LightRAG) and 2 from Microsoft's Graph Retrieval Augmented Generation (MGRAG). We examine these sub-models across 4 key criteria-comprehensiveness, diversity, empowerment, and directness-as well as a combination of all 4 which we call “over-all”. After 3 separate experiments, we observe that GraphRAGs have a clear advantage over RAGs for digesting complex language in this task. Within GraphRAGs, we observe that MGRAG has a slight advantage over LightRAG and naive RAG. We present the results in grouped bar charts to offer clear and accessible comparisons, enabling anyone interested to quickly make informed decisions about which methods best suit their business needs.