Legal Chunking: Evaluating Methods for Effective Legal Text Retrieval
Abstract
Legal document retrieval is heavily influenced by how documents are segmented, or “chunked,” for processing within Retrieval-Augmented Generation (RAG) systems. This paper investigates the effectiveness of three automated chunking techniques — Simple Text Splitting, Recursive Text Splitting using regular expressions (regex), and Semantic Chunking — within the legal domain, using the General Data Protection Regulation (GDPR) as a testbed. The chunking methods were evaluated based on their semantic relevance to a set of seventeen legal questions and their corresponding relevant sections, with performance measured using cosine similarity metrics. Results show that none of the methods consistently produced high semantic relevance on an individual chunk level: Git Hub link.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.