Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions

Excerpt

arXiv:2604.12138v4 Announce Type: replace Abstract: 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 the aleatoric uncertainty, inherent in opinion-rich content. The consequences go beyond technical limitations- due to risk of minority voice erasure