Towards Query-Agnostic RAG Evaluation via Query Coverage and Claim Verifiability

Abstract:Retrieval-augmented generation improves the factuality of large language models by grounding responses in retrieved evidence, yet existing evaluation frameworks struggle to provide consistent, fine-grained diagnostics across the diverse spectrum of user queries, ranging from close-ended fact-seeking to open-ended explanatory requests. We propose Q-CARE, a query-agnostic and fully reference-free framework that enables fine-grained assessment by decomposing queries into sub-queries and answers into atomic claims. Q-CARE establishes a unified evaluation principle based on query coverage and claim verifiability, yielding coverage-aware retriever metrics (C-Prec@k, C-nDCG@k) and claim-level generator metrics (Completeness, Conciseness, and Verifiableness). On a human-annotated benchmark spanning eight datasets, Q-CARE achieves higher correlation with human judgments than four existing RAG evaluation metrics, including RAGEval and RAGChecker, proving its effectiveness as a reliable, automated evaluation framework. Code and data are publicly available at this https URL.

Artificial Intelligence (cs.AI)

arXiv:2608.11238 [cs.AI]

(or arXiv:2608.11238v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.11238 Focus to learn more arXiv-issued DOI via DataCite

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