ARXIV:2605.30790 · RAG OPTIMIZATION · SUBMITTED 01 JUN · 20:29 UTC · FRESHNESS STALE

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On the impact of retrieved content representations in RAG Pipelines

Jonathan J Ross · Bevan Koopman · Anton van der Vegt · Guido Zuccon · arXiv

Investigating how retrieved content representations impact RAG pipeline accuracy by analyzing answer retention across various transformations.

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Pain Investigating how retrieved content representations impact RAG pipeline accuracy by analyzing answer retention across various transformations.

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PROBLEM

Investigating how retrieved content representations impact RAG pipeline accuracy by analyzing answer retention across various transformations. How retrieved content should be represented when the consumer is a large language model (LLM) rather than a…

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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…

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RAG Optimization moved forward this cycle; last verified June 2026. Public score 4.0/10.

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PainInvestigating how retrieved content representations impact RAG pipeline accuracy by analyzing answer retention across various transformations.

Evidence0 refs | 3 sources | 50% coverage

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Analysis summary

Investigating how retrieved content representations impact RAG pipeline accuracy by analyzing answer retention across various transformations.

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Investigating how retrieved content representations impact RAG pipeline accuracy by analyzing answer retention across various transformations.

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Investigating how retrieved content representations impact RAG pipeline accuracy by analyzing answer retention across various transformations.

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On the impact of retrieved content representations in RAG Pipelines

On the impact of retrieved content representations in RAG Pipelines

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