When Is Complex Chunking Worth It? A Multi-Objective Evaluation of Chunking Methods at Scale

2026-08-17

AI Summary

This study addresses the lack of comprehensive evaluations balancing effectiveness and system overhead in chunking strategies for long-document retrieval. We systematically benchmark eight strategies across multi-scale corpora, quantifying both retrieval performance and operational costs, including indexing throughput and latency. Our findings reveal that complex chunking methods do not necessarily outperform simpler baselines; high computational costs often yield inconsistent gains alongside significant operational disparities. Consequently, this work establishes chunking as a multi-objective design decision, emphasizing that optimal strategies are highly contingent upon specific models, datasets, and evaluation metrics. These insights provide empirical evidence to guide trade-off optimization in practical deployment scenarios, challenging the assumption that increased algorithmic complexity inherently translates to superior system-level utility in document retrieval pipelines.

Abstract

Dense retrieval is commonly evaluated on benchmarks that represent each document with a single embedding, even though real-world retrieval systems often index long documents that require chunking. In these settings, the chosen chunking method not only affects retrieval quality, but also indexing throughput, query latency, and memory usage. Prior comparisons of chunking strategies have mainly focused on retrieval performance, leaving operational trade-offs underexplored. To address these issues, we evaluate eight representative chunking strategies across two scalable corpora, three embedding models, and multiple corpus sizes, measuring both retrieval effectiveness and system-level costs. Our results show that computationally expensive methods rarely provide consistent gains over simpler chunking. Instead, the best performing strategy depends on the embedding model, dataset, corpus size, and target retrieval metric. Methods with similar performance can also differ substantially in operational cost, showing that chunking should be seen as a multi-objective design decision.

Problem

• Dense Retrieval
• Chunking Strategies
• Multi-Objective Evaluation
• Operational Cost
• Long Documents

Innovation

• Multi-Objective Evaluation
• Chunking Strategies
• Operational Trade-offs
• Dense Retrieval
• System-level Costs