arXiv AI June 9, 2026 By Chenyu Wang, Yueyuan Li, Yingmin Liu, Yang Shu

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 14

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

arXiv:2607. 10491v1 Announce Type: new Abstract: Retrieval-augmented generation grounds large language models in external evidence, but most pipelines still treat retrieved passages as deterministic and mutually consistent context.

By S M Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha

llms rag nlp safety

arXiv AI

Sep 10

A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems

arXiv:2609.07075v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its r...

By Ramon Gonzalez, Antonio Diaz

rag

arXiv AI

Jun 26

ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence

arXiv:2606. 26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting and contradicting evidence coexist.

By Siyi Liu, Aaron Halfaker, Dan Roth, Patrick Xia

llms benchmarks

arXiv Machine Learning

Jun 2

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

arXiv:2606. 00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence.

By Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter, Anuj Sharma

llms rag

arXiv AI

Jul 28

Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach

arXiv:2607. 22584v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or credibility.

By Yuktha Tata Koganti, Hugo Garrido-Lestache Belinchon

rag safety

arXiv AI

Aug 5

Diversity is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA

arXiv:2608. 03177v1 Announce Type: new Abstract: How can question answering (QA) systems determine whether a query is ambiguous?

By Jiwon Lee, Yong-chan Park, Jungin Hong, U Kang

nlp benchmarks

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Related stories

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

A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems

ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach

Diversity is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA