ERAG is introduced, an uncertainty-aware RAG framework that converts retrieved chunks into probabilistic evidence before generation and suggests that evidential modeling is a practical mechanism for trustworthy information processing in foundation-model-based retrieval systems.
Abstract
Retrieval-augmented generation grounds large language models in external evidence, but most pipelines still treat retrieved passages as deterministic and mutually consistent context. In open information environments, retrieved sources may disagree because of temporal drift, source error, ambiguity, or genuine uncertainty. This paper introduces ERAG, an uncertainty-aware RAG framework that converts retrieved chunks into probabilistic evidence before generation. A lightweight evaluator extracts candidate claims and maps chunk-level support to Dirichlet evidence. A conflict-preserving Dempster-Shafer fusion rule then transfers unresolved disagreement into epistemic uncertainty rather than normalizing it away. The generator is routed to direct answering, conflict-aware answering, or abstention according to the fused uncertainty score. Experiments on CRAG, ConflictQA, and MuSiQue show that ERAG remains competitive with the strongest matched baseline on standard question answering while improving behavior under conflict. On the CRAG ambiguous subset, hallucination decreases from 45.3% for Corrective RAG to a human-calibrated estimate of 34.8%, conflict resolution increases from 35.2% to 51.2%, and expected calibration error improves to 0.122. These results suggest that evidential modeling is a practical mechanism for trustworthy information processing in foundation-model-based retrieval systems.
Large Language Models (LLMs) can generate fluent and convincing responses, but fluency does not guarantee
factual correctness. Hallucination occurs when a model produces information that is false, unsupported, or inconsistent
with available evidence. This paper reviews why hallucinations arise andexamine Retrieval-Augmented Generation (RAG)
as a practical mitigation strategy. The discussion focuses on prediction-based generation, missing or outdated knowledge,
ambiguous queries, and the absence of automatic verification. RAG addresses these limitations by retrieving relevant
information from an external knowledge source and supplying it to the generator as contextual evidence. The paper
describes the major stages of a RAG pipeline, including document ingestion, chunking, embeddings, vector storage,
retrieval, context augmentation, and answer generation. It also examines the conditions under which RAG can fail,
including poor retrieval, incomplete knowledge bases, unreliable sources, weak ranking, and incorrect interpretation of
retrieved context. Finally, the paper discusses evaluation dimensions such as context relevance, answer faithfulness, and
answer relevance, and outlines applications of RAG in education, enterprise search, technical support, research assistance,
and document question answering. The analysis concludes that RAG should be viewed as a grounding and evidence-access
mechanism rather than a guarantee of hallucination-free generation
Shyalaja L. N., Shantinath Patil, Pruthviraj S. R. et al.· International Journal for Re...· 0 citations
RAAD (Retrieval-Augmented Ambiguity Detection), a lightweight framework that detects question ambiguity by identifying semantically incompatible answers grounded in retrieved evidence, generalizes strongly across datasets, outperforming reported baselines on multiple benchmarks.
Parth Patel, Sarah Kamoun, Bita Azad et al.· International Conference on...· 0 citations
Retrieval-Augmented Generation (RAG) improves the factuality of large language models with external knowledge, yet conflicting evidence remains a fundamental challenge in dynamic and adversarial environments. Existing approaches often treat conflicts as static inconsistencies and select more reliable knowledge, overlooking that the same conflict may arise from legitimate knowledge evolution, malicious manipulation, or unresolved uncertainty. We formulate conflict origin attribution as a new problem in RAG: identifying which explanation of conflicting evidence is supported by observable context rather than simply which fact should be trusted. We propose EvoTrustRAG, a training-free framework for evolution-aware conflict attribution and evidence handling before answer generation. EvoTrustRAG represents span-grounded retrieved facts as a conflict evidence graph, evaluates grounded evolution and directional intervention hypotheses using temporal relations, support structure, and auxiliary consistency, and projects local decisions onto a globally consistent explanation of each conflict group. The attribution determines whether earlier and later states are preserved as temporal knowledge, an intervention candidate is separated from the primary context, or an unresolved conflict remains visible to the generator. Unlike provenance-based approaches focused on post-hoc analysis, EvoTrustRAG determines during inference whether conflicting evidence follows plausible knowledge evolution, exhibits intervention-like support, or cannot be reliably attributed. Experiments show that EvoTrustRAG achieves 81.4% average accuracy on benchmark-native conflict settings, improves attribution macro-F1 from 72.2% to 79.1% over the strongest baseline, and reduces the error rate under the strongest coordinated attack from 31.2% to 16.0%.
Xiao-Rui Nie, Hongwei Li, Shenghao Wu et al.· 0 citations
The proposed ReliableRAG is the first reliability-driven framework that mitigates deceptive misinformation in multi-hop QA through fine-grained evaluation of individual triples, and quantifies triple reliability by combining query-triple semantic relevance with triple credibility.
Jingjing Jiang, Xuan Wu, Wenhao Song et al.· 0 citations
Visual retrieval-augmented generation (RAG) commonly expands the retrieved evidence set to improve answer-page coverage, implicitly assuming that all available evidence should be passed to the generator. We show that this assumption does not hold for diffusion language models (DLMs): retrieving more pages increases answer-page recall, whereas unconditionally passing all retrieved pages to the generator often reduces answer accuracy, primarily because of semantic conflict. A latent-source analysis explains this mismatch through source-coherence loss in parallel denoising, where position-wise proposals can combine incompatible visual sources into unsupported answers. We further find that such interference is already visible in the first-step answer-block distribution, making it possible to assess evidence before decoding. To preserve retrieval coverage while limiting harmful visual exposure, we propose the Entropy-Based Candidate Filter (ECF), a training-free evidence-admission framework. To reduce irrelevant content within individual candidates, ECF constructs multi-granularity evidence units; to identify beneficial additional evidence, it uses blank-controlled block confidence and retrieval rank to determine whether and which candidate should enter the final context. Across three multimodal DLMs and five visual QA benchmarks, ECF improves answer accuracy by 2.62 percentage points on average over the strongest fixed top-$k$ input and, with LLaDA2.0-Uni, by 2.37 percentage points on average over the best competing training-free result for each dataset. These results show that broader retrieval benefits visual DLM-RAG through selective evidence admission rather than unconditional evidence expansion. Code is publicly available at https://github.com/wjkuser/ECF.
Jiankun Wang, Yi-Sen Gao, Ziwei Zhang et al.· 0 citations
Volume-based accuracy rewards retrieval-augmented generation (RAG) systems for guessing: a system that answers everything outscores one that declines when its knowledge base cannot support an answer. Building on the confidence-target analysis of Kalai et al. (2025), we present a penalty-aware evaluation framework for deployed RAG products, combining (i) asymmetric scoring (correct +1, wrong -4, abstain 0), (ii) knowledge-gap canaries, questions whose answers are verifiably absent from the knowledge base, so that any answer constitutes ungrounded generation from parametric memory, and (iii) a failure-attribution pipeline that separates retrieval, generation, and abstention-policy failures. Applying the framework to three commercial RAG systems and a no-retrieval baseline on SimpleQA-Verified (1,000 questions x 3 repeats, graded blind by a cross-family three-judge panel with 98.9% unanimity), we find that accuracy when answering is closely clustered across systems (97.0-98.0%), while canary violation rates differ roughly sixfold (16.7% vs. 98.1%). The systems are separated less by what they answer correctly than by whether they answer at all when they should not, and penalty-aware scoring reorders the volume-based ranking accordingly; the reordering is stable across penalty settings from k=1 to k=9. All code, configurations, transcripts, and judge votes are released for independent audit.
Alden Do Rosario, Hussein Younes, Felipe Pires· 0 citations
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