EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning

• URL: http://arxiv.org/abs/2607.10491v1
• Date: Sat, 11 Jul 2026 21:56:15 GMT
• Title: EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning
• Abstract summary: This paper introduces ERAG, an uncertainty-aware RAG framework that converts retrieved chunks into probabilistic evidence before generation.<n>Experiments on CRAG, ConflictQA, and MuSiQue show that ERAG remains competitive with the strongest matched baseline on standard question answering.
• Score: 0.2730969268472861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• One Reflection Is Not Enough: Self-Correcting Autonomous Research via Multi-Hypothesis Failure Attribution [85.65263343588026]
  We propose SAGE, a Self-correcting, Autonomous, Grounded Experimenter.<n>Its core mechanism, Multi-Hypothesis Failure Attribution (MHFA), treats recovery as a structured causal diagnosis.<n>On a 12-topic, 5-domain benchmark, SAGE increases metrics-bearing outputs from 42% to 92% over a baseline reflection.
  arXiv  Detail & Related papers  (2026-06-30T10:54:16Z)
• Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict [7.091145002779077]
  Context-Driven Decomposition (CDD) is a belief-decomposition probe that operates at inference time.<n>It serves as an intervention mechanism for controlled retrieval conflict.<n>These three patterns identify context-compliance as a structural axis along which standard RAG can be probed.
  arXiv  Detail & Related papers  (2026-05-14T07:14:19Z)
• To Fuse or to Drop? Dual-Path Learning for Resolving Modality Conflicts in Multimodal Emotion Recognition [87.22988227382329]
  Multimodal emotion recognition (MER) benefits from combining text, audio, and vision, yet standard fusion often fails when modalities conflict.<n>We propose Dual-Path Conflict Resolution (DCR), a unified framework that learns when to fuse and when to drop modalities.
  arXiv  Detail & Related papers  (2026-05-06T13:11:33Z)
• Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation [45.13215113490545]
  GuarantRAG is a framework that explicitly decouples reasoning from evidence integration.<n> Experiments on five QA benchmarks demonstrate that GuarantRAG improves accuracy by up to 12.1%.
  arXiv  Detail & Related papers  (2026-04-09T09:52:21Z)
• ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation [19.616905640490252]
  We propose a new framework called ERA (Evidence-based Reliability Alignment) to enhance abstention behavior in RAG systems.<n>Our approach significantly outperforms baselines, optimizing the trade-off between answer coverage and abstention with superior calibration.
  arXiv  Detail & Related papers  (2026-02-24T01:45:51Z)
• Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation [64.09110141948693]
  Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise.<n>We propose BAR-RAG, which reframes the reranker as a boundary-aware evidence selector that targets the generator's Goldilocks Zone.<n>Bar-RAG consistently improves end-to-end performance under noisy retrieval.
  arXiv  Detail & Related papers  (2026-02-03T16:08:23Z)
• Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation [12.469991196570106]
  TCR (Transparent Conflict Resolution) disentangles semantic match and factual consistency via dual contrastive encoders.<n>It raises knowledge-gap recovery by +21.4 pp and cuts misleading-context overrides by -29.3 pp, while adding only 0.3% parameters.<n>The signals align with human judgements and expose temporal decision patterns.
  arXiv  Detail & Related papers  (2026-01-11T10:08:49Z)
• 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)
• Retrieval-Augmented Generation with Conflicting Evidence [57.66282463340297]
  Large language model (LLM) agents are increasingly employing retrieval-augmented generation (RAG) to improve the factuality of their responses.<n>In practice, these systems often need to handle ambiguous user queries and potentially conflicting information from multiple sources.<n>We propose RAMDocs (Retrieval with Ambiguity and Misinformation in Documents), a new dataset that simulates complex and realistic scenarios for conflicting evidence for a user query.
  arXiv  Detail & Related papers  (2025-04-17T16:46:11Z)
• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications.<n>Our research identifies two critical latent factors affecting RAG's confidence in its predictions.<n>We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)

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.