[2603.25333] Adaptive Chunking: Optimizing Chunking-Method Selection for RAG

Computer Science > Computation and Language arXiv:2603.25333 (cs) [Submitted on 26 Mar 2026] Title:Adaptive Chunking: Optimizing Chunking-Method Selection for RAG Authors:Paulo Roberto de Moura Júnior, Jean Lelong, Annabelle Blangero View a PDF of the paper titled Adaptive Chunking: Optimizing Chunking-Method Selection for RAG, by Paulo Roberto de Moura J\'unior and 2 other authors View PDF Abstract:The effectiveness of Retrieval-Augmented Generation (RAG) is highly dependent on how documents are chunked, that is, segmented into smaller units for indexing and retrieval. Yet, commonly used "one-size-fits-all" approaches often fail to capture the nuanced structure and semantics of diverse texts. Despite its central role, chunking lacks a dedicated evaluation framework, making it difficult to assess and compare strategies independently of downstream performance. We challenge this paradigm by introducing Adaptive Chunking, a framework that selects the most suitable chunking strategy for each document based on a set of five novel intrinsic, document-based metrics: References Completeness (RC), Intrachunk Cohesion (ICC), Document Contextual Coherence (DCC), Block Integrity (BI), and Size Compliance (SC), which directly assess chunking quality across key dimensions. To support this framework, we also introduce two new chunkers, an LLM-regex splitter and a split-then-merge recursive splitter, alongside targeted post-processing techniques. On a diverse corpus spanning legal, techni...