On the impact of retrieved content representations in RAG Pipelines
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,...
| Authors | Jonathan J Ross, Bevan Koopman, Anton van der Vegt et al. |
|---|---|
| Primary category | cs.IR |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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