Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2609.08267v1
• Date: Tue, 08 Sep 2026 05:22:29 GMT
• Title: Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation
• Abstract summary: Hallucination detection is crucial for large language models (LLMs)<n>Current detection methods depend on internal signals like uncertainty and self-consistency checks.<n>Retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time.
• Score: 17.29201876137025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertainty and self-consistency checks, using the model's pre-trained knowledge to identify unreliable outputs. However, pre-trained knowledge may become outdated and has coverage limitations, especially for specialized or recent information. To address these limitations, retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time, grounding outputs beyond the model's parametric knowledge. In this paper, we target a critical and practical learning problem RAG-based hallucination detection (RHD), where RAG is employed to enhance hallucination detection by addressing information updating challenges. To address RHD, we propose a novel method Evidence-Aligned Entity Verification (EAEV), which detects entity-level hallucinations by leveraging RAG to align generated entities with retrieved evidence contexts. Specifically, EAEV evaluates entity-evidence alignment through three complementary dimensions and introduces counterfactual stability analysis to ensure robust alignments under evidence perturbations. Experiments across multiple RAG benchmarks demonstrate that EAEV achieves consistent improvements over existing methods with strong generalization capabilities.

Related papers

• Towards Trustworthy Depression Estimation via Disentangled Evidential Learning [50.22167852149165]
  We propose EviDep, an evidential learning framework that jointly quantifies depression severity.<n>EviDep enforces strict information integrity to guarantee robust evidence synthesis.<n>It achieves state-of-the-art predictive accuracy and superior uncertainty calibration, delivering a robust fail-safe mechanism for trustworthy clinical screening.
  arXiv  Detail & Related papers  (2026-04-17T13:27:11Z)
• Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG [8.18791900871137]
  Retrieval-Augmented Generation aims to reduce hallucinations by grounding answers in retrieved evidence.<n>Existing evaluations focus on answer-level or passage-level accuracy, offering limited insight into how evidence is used during generation.<n>We introduce a facet-level diagnostics framework for QA that decomposes each input question into atomic reasoning facets.
  arXiv  Detail & Related papers  (2026-04-10T09:59:43Z)
• Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification [60.18369393468405]
  Existing verifiers usually underperform owing to a lack of domain knowledge and limited calibration.<n>GLEAN compiles expert-curated protocols into trajectory-informed, well-calibrated correctness signals.<n>We empirically validate GLEAN with agentic clinical diagnosis across three diseases from the MIMIC-IV dataset.
  arXiv  Detail & Related papers  (2026-03-03T09:36:43Z)
• From Detection to Diagnosis: Advancing Hallucination Analysis with Automated Data Synthesis [7.3390265169910665]
  Hallucinations in Large Language Models (LLMs) represent a core obstacle to their reliable deployment in critical domains.<n>To address this limitation, a new research paradigm is proposed, shifting from "detection" to "diagnosis"
  arXiv  Detail & Related papers  (2025-12-31T05:06:35Z)
• Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders [39.5490415037017]
  Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs) by grounding outputs in retrieved evidence.<n>Existing hallucination detection methods for RAG often rely on large-scale detector training.<n>We introduce RAGLens, a lightweight hallucination detector that accurately flags unfaithful RAG outputs.
  arXiv  Detail & Related papers  (2025-12-09T18:33:22Z)
• Factuality and Transparency Are All RAG Needs! Self-Explaining Contrastive Evidence Re-ranking [0.2864713389096699]
  This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER)<n>CER restructures retrieval around factual evidence by fine-tuning embeddings with contrastive learning and generating token-level attribution rationales for each retrieved passage.<n>We evaluated our method on clinical trial reports, and initial experimental results show that CER improves retrieval accuracy, mitigates the potential for hallucinations in RAG systems, and provides transparent, evidence-based retrieval that enhances reliability, especially in safety-critical domains.
  arXiv  Detail & Related papers  (2025-12-04T17:24:35Z)
• An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination [6.001574550157585]
  Unsupervised anomaly detection methods typically assume clean training data, yet real-world datasets often contain undetected or mislabeled anomalies.<n>We propose EPHAD, a test-time adaptation framework that updates the outputs of AD models trained on contaminated datasets using evidence gathered at test time.
  arXiv  Detail & Related papers  (2025-10-24T09:45:26Z)
• Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation [108.13261761812517]
  We introduce FRANQ (Faithfulness-based Retrieval Augmented UNcertainty Quantification), a novel method for hallucination detection in RAG outputs.<n>We present a new long-form Question Answering (QA) dataset annotated for both factuality and faithfulness.
  arXiv  Detail & Related papers  (2025-05-27T11:56:59Z)
• Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs [12.923119372847834]
  This paper presents the first systematic evaluation of Retrieval-Augmented Generation (RAG) models for fact-checking.<n>Experiments reveal critical vulnerabilities in state-of-the-art RAG methods, particularly in resolving conflicts stemming from differences in media source credibility.<n>Our results show that effectively incorporating source credibility significantly enhances the ability of RAG models to resolve conflicting evidence and improve fact-checking performance.
  arXiv  Detail & Related papers  (2025-05-23T11:35:03Z)
• Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation [78.78421340836915]
  We systematically investigate reference-free hallucination detection in open-domain long-form responses.<n>Our findings reveal that internal states are insufficient for reliably distinguishing between factual and hallucinated content.<n>We introduce a new paradigm, named RATE-FT, that augments fine-tuning with an auxiliary task for the model to jointly learn with the main task of hallucination detection.
  arXiv  Detail & Related papers  (2025-05-18T07:10:03Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation [91.20492150248106]
  We investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases.<n>We propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs.<n> Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory.
  arXiv  Detail & Related papers  (2025-02-21T15:50:41Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.