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Towards Query-Agnostic RAG Evaluation via Query Coverage and Claim Verifiability — arXiv:2608.11238

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.11238
License (as archived): CC0-1.0
Archived: 2026-10-19T05:15:14.565Z
SHA-256 of the archived text: 1b0b6e329ad81f9b102872198463bc86750cef74b75a37a1ffb3ddef83c245ca
Stable URL of this page: https://metraipolis.com/documento/dc_bd9e76e487cf

How to cite

Towards Query-Agnostic RAG Evaluation via Query Coverage and Claim Verifiability — arXiv:2608.11238 — arXiv (en), https://arxiv.org/abs/2608.11238 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd9e76e487cf (Metraipolis archive dc_bd9e76e487cf, sha256 1b0b6e329ad81f9b102872198463bc86750cef74b75a37a1ffb3ddef83c245ca)

BibTeX

@misc{dcbd9e76e487cf,
  title = {Towards Query-Agnostic RAG Evaluation via Query Coverage and Claim Verifiability — arXiv:2608.11238},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.11238},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd9e76e487cf; sha256 1b0b6e329ad81f9b102872198463bc86750cef74b75a37a1ffb3ddef83c245ca},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.11238. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-13 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.11238 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:08:46.609Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).