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.
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 https://github.com/DISL-Lab/Q-CaRE-COLM-26.
These findings establish learned sparse retrieval as a highly impactful design choice in automated fact-checking, with retrieval quality serving as a critical determinant of end-to-end performance in the studied systems.
Ritvik Setty, Vinay Setty· International Conference on...· 0 citations
Results show that SQL verification can be performed with a lightweight learned model while retaining feature-level evidence for inspecting and diagnosing its predictions, and feature attribution shows that the model relies on both semantic grounding and deterministic SQL-structure signals.
N. Shukla, Debasmita Panda, Srutanik Bhaduri et al.· 0 citations
Retrieval-augmented generation treats an external corpus as inference evidence, allowing injected documents to promote attacker-chosen claims. Existing detectors depend on trusted references, specific attack artifacts, or global thresholds sensitive to corpus topology. We present RAGSieve, a self-referenced detection framework that constructs its reference from the inspected system. RAGSieve-Query (RSQ) performs query-local contrast, scoring top-five candidates against ranks 6-20 of the same retrieval to detect answer-anchor concentration and carrier transitions. RAGSieve-Graph (RSG) performs corpus-local contrast, comparing each document's semantically similar but lexically distinct neighbors with its local baseline to detect coordinated density before queries arrive. Across three QA datasets and six poisoning constructions, RSQ achieves 95.2% AUROC and detects 82.2% of poison at 5% clean-document removal, versus 81.1%/52.5% for GMTP. RSG achieves 93.3%/79.8%, versus 79.4%/37.6% for CleanBase. Joint deployment reduces attack success from 67.4% to 14.0% while retaining 41.3% F1 on unpoisoned retrieval, demonstrating practical protection at both corpus ingestion and query time without poison labels or trusted corpora. Source code is available at https://github.com/XrazyMee/RAGSieve.
It is proved that, under an honest-majority assumption and a representation-level separation condition, RAGSentinel exactly recovers a poison-free majority-sized context.
Yueyang Quan, Anjun Gao, Yu Xia et al.· 0 citations
LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, violating the information-access boundary of pre-search users. Existing validation metrics, including overlap, diversity, and effectiveness, cannot distinguish rare human-tail variation from candidate answer-side intrusion. We introduce concept provenance, a framework that assigns query concepts to backstory-supported, human-central, human-tail, and candidate answer-side zones, operationalizing a boundary that retrieval metrics alone cannot detect. Applying concept provenance to 77,004 queries across 100 UQV100 topics, 8 LLMs, and 5 prompt conditions with two extraction pipelines, we obtain a cross-pipeline token-HCIR Spearman rho of 1.0 over five condition means. Candidate answer-side concepts constitute 7.40 percent of non-generic concepts and appear in 97 of 100 topics, with topic explaining approximately 67 percent of variance. Human validation yields 68.2 percent relaxed precision, revealing two mechanisms: knowledge intrusion at 45.5 percent and deployment intrusion at 45.0 percent. Diagnostic probes show disproportionate localized retrieval effects, with deletion effect size d = -0.47 compared with d = -0.34 for random deletion, but these concepts explain less than 2 percent of aggregate evaluation variance. Concept provenance therefore serves as a boundary-compliance diagnostic rather than an evaluation-shift predictor. Under the tested conditions, no prompt condition eliminates intrusion; post-generation concept-provenance selection achieves 99 percent elimination.
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi et al.· 0 citations
TKFQA, a factuality consistency benchmark comprising 10,130 question-answering (QA) pairs grounded in tables, texts, and knowledge graphs, is introduced and ORLF, an LLM-agnostic training framework that models cross-context topological relations through knowledge-specific latent vectors is proposed.
Shibo Chu, Yuze Liu, Tiehua Zhang et al.· 0 citations