mlsanityData to 7 Oct 2026

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

arXiv 2605.17301 ↗First posted 17 May 2026cs.CL cs.AIcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice. We present ConflictRAG, a conflict-aware RAG framework that detects, classifies, and resolves knowledge conflicts prior to answer generation. The framework introduces three contributions: (1) a two-stage conflict detection module combining a lightweight embedding-based MLP classifier with selective LLM refinement, reducing API costs by 62% while maintaining 90.8% detection accuracy; (2) an Entropy-TOPSIS framework for data-driven source credibility assessment, improving selection accuracy by 7.1% over manual heuristics; and (3) a Conflict-Aware RAG Score (CARS) for diagnostic evaluation of conflict-handling capabilities. Experiments on three benchmarks against six baselines demonstrate 88.7% conflict-detection F1 and consistent 5.3--6.1% correctness gains over the strongest conflict-aware baseline, with the pipeline transferring effectively across backbone LLMs.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 30 references that are in the map. First posted on arXiv 17 May 2026.

Builds on

24 of 30 references.

Closest ancestors · 9

Same direction, newest first

  1. 2026Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation11 Jan
  2. 2025TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs13 Nov
  3. 2025FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation10 Jun
  4. 2025DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs10 Jun
  5. 2025Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs23 May
  6. 2025Retrieval-Augmented Generation with Conflicting Evidence17 Apr
  7. 2024Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models22 Feb
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct
  9. 2023Benchmarking Large Language Models in Retrieval-Augmented Generation4 Sep

Other references · 15

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2024GPT-4o System Cardcited by 4,467
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  6. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  7. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  8. 2017Supervised Learning of Universal Sentence Representations from Natural Language Inference Datacited by 428
  9. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  10. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 385
  11. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 349
  12. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
3 more
  1. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  2. 2024Corrective Retrieval Augmented Generationcited by 187
  3. 2023Resolving Knowledge Conflicts in Large Language Modelscited by 49

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation
  2. 2025Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
  3. 2025From Facts to Conclusions : Integrating Deductive Reasoning in Retrieval-Augmented LLMs
  4. 2025Conflict-Aware Soft Prompting for Retrieval-Augmented Generation
  5. 2026EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning
  6. 2026X-MADAM-RAG: Diagnosing and Handling Chinese-English Evidence Conflict in Retrieval-Augmented Generation
  7. 2024WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
  8. 2025Contradiction Detection in RAG Systems: Evaluating LLMs as Context Validators for Improved Information Consistency