Who Benefits from RAG? The Role of Exposure, Utility and Attribution Bias
Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have achieved substantial improvements in accuracy by grounding their responses in external documents that are relevant to the user's query. However, relatively little work has investigated the impact of RAG in terms of fairness. Particularly, it is not yet known if queries that are associated with certain groups within a fairness category systematically receive higher accuracy, or accuracy improvements in RAG systems compared to LLM-only, a phenomenon we refer to as query group fairness. In this work, we conduct extensive experiments to investigate the impact of three key factors on query group fairness in RA...
| Authors | Mahdi Dehghan, Graham McDonald |
|---|---|
| Primary category | cs.IR |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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