A Systematic Investigation of Document Chunking Strategies and Embedding Sensitivity
We present the first large-scale, cross-domain evaluation of document chunking strategies for dense retrieval, addressing a critical but underexplored aspect of retrieval-augmented systems. In our study, 36 segmentation methods spanning fixed-size, semantic, structure-aware, hierarchical, adaptive, and LLM-assisted approaches are benchmarked across six diverse knowledge domains using five different embedding models. Retrieval performance is assessed using graded relevance scores from a state-of-the-art LLM evaluator, with Normalised DCG@5 as the primary metric (complemented by Hit@5 and MRR). Our experiments show that content-aware chunking significantly improves retrieval effectiveness o...
| Authors | Muhammad Arslan Shaukat, Muntasir Adnan, Carlos C. N. Kuhn |
|---|---|
| Primary category | cs.CL |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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