Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

• URL: http://arxiv.org/abs/2604.24334v1
• Date: Mon, 27 Apr 2026 11:23:39 GMT
• Title: Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering
• Abstract summary: This study explores chunk filtering strategies to reduce the indexed corpus while preserving retrieval quality.<n>Retrieval performance is evaluated using a token-based framework based on precision, recall, and intersection-over-union metrics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Standard Retrieval-Augmented Generation (RAG) chunking methods often create excessive redundancy, increasing storage costs and slowing retrieval. This study explores chunk filtering strategies, such as semantic, topic-based, and named-entity-based methods in order to reduce the indexed corpus while preserving retrieval quality. Experiments are conducted on multiple corpora. Retrieval performance is evaluated using a token-based framework based on precision, recall, and intersection-over-union metrics. Results indicate that entity-based filtering can reduce vector index size by approximately 25% to 36% while maintaining high retrieval quality close to the baseline. These findings suggest that redundancy introduced during chunking can be effectively reduced through lightweight filtering, improving the efficiency of retrieval-oriented components in RAG pipelines.

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