Title: Assessing Chunking Techniques in Retrieval-Augmented Generation: Balancing Efficacy with Computational Overhead and Constraints

Abstract: Retrieval-Augmented Generation (RAG) has proven instrumental in boosting the capabilities of Large Language Models (LLMs). A critical component within RAG architectures is the chunking mechanism. While fixed-size and semantic splitting have long served as the baseline methods, there is a burgeoning interest in alternative strategies, resulting in numerous proposed techniques that assert superior performance. However, many of these novel approaches are highly specialized for particular datasets or applications, lacking robust validation across varied contexts. This specialization complicates direct comparisons and makes it difficult to gauge their true relative advantages. To our knowledge, this paper presents the first comprehensive evaluation of a broad spectrum of chunking methodologies, highlighting the inherent difficulties associated with these strategies in RAG frameworks. We challenge the common perception of chunking as a trivial preprocessing task, demonstrating instead that it engenders significant, yet frequently neglected, complications.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC