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Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions
Retrieval-Augmented Generation (RAG) systems are built on an unexamined assumption - that queries have correct answers and retrieval should converge toward them. This position paper argues that this creates a factual bias where RAG systems optimize for reducing epistemic uncertainty while ignoring t
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