Evaluating Chunking Strategies for Retrieval

A comprehensive framework for generating domain and dataset-specific evaluations has been introduced to more accurately capture critical properties such as information density via Intersection over Union (IoU) in practical AI retrieval applications. The study evaluated various chunking strategies across several popular domains, demonstrating the framework's effectiveness in capturing variations in retrieval performance due to chunking choices. Notably, the embedding-model aware chunking strategy developed within this work consistently yielded strong results, alongside another novel strategy that also performed well. The evaluation highlighted the importance of overlapping chunks for high recall in smaller contexts and identified limitations in the synthetic evaluation pipeline related to the lack of creativity in question generation by large language models (LLMs). Future work aims to address these limitations by expanding dataset size, increasing diversity, and potentially blending human-annotated data with synthetic data. Additionally, the time required to execute chunking methods, ranging from instantaneous to several minutes, remains a practical consideration not accounted for in this study.

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