URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models

ArXi:2603.19281v1 Announce Type: cross Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. However, current RAG evaluations concentrate primarily on correctness, which may not fully capture the impact of retrieval on LLM uncertainty and reliability. To bridge this gap, we