On the impact of retrieved content representations in RAG Pipelines

• URL: http://arxiv.org/abs/2605.30790v1
• Date: Fri, 29 May 2026 03:26:41 GMT
• Title: On the impact of retrieved content representations in RAG Pipelines
• Abstract summary: How retrieved content should be represented when the consumer is a large language model (LLM) rather than a human is less well understood.<n>Recent work has proposed transformations of retrieved content and identified properties that affect generation.<n>We compare an original baseline against thirteen transformations spanning selection, summarisation, and reformulation.<n>We find that answer retention is the primary determinant of generator accuracy.
• Score: 33.306933250440174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) supplements a language model's input with retrieved documents, yet most RAG pipelines inherit retrieval components designed for human readers. How retrieved content should be represented when the consumer is a large language model (LLM) rather than a human is less well understood. Recent work has proposed transformations of retrieved content and identified properties that affect generation, but each examines a single transformation or property in isolation, leaving open which features of a document's representation matter most. We address this with a controlled comparison: holding retrieval fixed, we vary only the representation of retrieved documents, comparing an original baseline against thirteen transformations spanning selection, summarisation, and reformulation, in query-dependent and query-independent variants. Across these fourteen representations we measure question-answering accuracy for four generators, and for each representation we also measure answer retention: whether a known answer-bearing document still supports its answer after transformation. We find that answer retention is the primary determinant of generator accuracy; notably, when retention is high, a representation's wording, structure, length, and query-dependence have limited effect. This suggests that accuracy gains attributed to specific mechanisms in prior work may be partly explained by how well those mechanisms preserve answer-bearing content, an attribution that cannot be settled without controlling for retention.

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