Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts
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Computer Science > Information Retrieval
Title:Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts
Abstract:Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside their parameters. We evaluate if cluster-based semantic chunking improves retrieval and answer quality compared to fixed-size and recursive chunking evaluating on long, structured academic theses using the Retrieval Augmented Generation Assessment (RAGAs) framework. RAGAs based faithfulness shows limited reliability in this setup. Performance on fixed versus document specific questions varied substantially, likely related to the formatting of documents and preprocessing. Under the tested configuration, cluster-based chunking did not outperform simpler strategies.
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.01852 [cs.IR] |
| (or arXiv:2607.01852v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01852
arXiv-issued DOI via DataCite (pending registration)
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