mlsanityData to 14 Oct 2026

Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation

arXiv 2601.06842 ↗First posted 11 Jan 2026cs.AIcited by 6

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

Abstract (arXiv)

Large language models (LLMs) equipped with retrieval--the Retrieval-Augmented Generation (RAG) paradigm--should combine their parametric knowledge with external evidence, yet in practice they often hallucinate, over-trust noisy snippets, or ignore vital context. We introduce TCR (Transparent Conflict Resolution), a plug-and-play framework that makes this decision process observable and controllable. TCR (i) disentangles semantic match and factual consistency via dual contrastive encoders, (ii) estimates self-answerability to gauge confidence in internal memory, and (iii) feeds the three scalar signals to the generator through a lightweight soft-prompt with SNR-based weighting. Across seven benchmarks TCR improves conflict detection (+5-18 F1), raises knowledge-gap recovery by +21.4 pp and cuts misleading-context overrides by -29.3 pp, while adding only 0.3% parameters. The signals align with human judgements and expose temporal decision patterns.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 30 of its 47 references that are in the map. First posted on arXiv 11 Jan 2026.

Builds on

30 of 47 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning14 Oct
  2. 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales19 Jun
  3. 2024Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training31 May
  4. 2024Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models22 Feb
  5. 2023Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts29 Sep

Other references · 25

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  3. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  4. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  5. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 356
  6. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 341
  7. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
  8. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  9. 2022Large Language Models with Controllable Working Memorycited by 169
  10. 2022TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Modelscited by 119
  11. 2023The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversationcited by 109
  12. 2022DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answeringcited by 96
13 more
  1. 2024Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Modelscited by 91
  2. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 90
  3. 2024ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLMcited by 73
  4. 2024When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentationcited by 59
  5. 2023Characterizing Mechanisms for Factual Recall in Language Modelscited by 48
  6. 2024IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neuronscited by 38
  7. 2025Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understandingcited by 28
  8. 2023FT2TF: First-Person Statement Text-To-Talking Face Generationcited by 26
  9. 2024Swift Sampler: Efficient Learning of Sampler by 10 Parameterscited by 25
  10. 2023Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignmentcited by 19
  11. 2025Learning Musical Representations for Music Performance Question Answeringcited by 17
  12. 2025Does Bigger Mean Better? Comparitive Analysis of CNNs and Biomedical Vision Language Modles in Medical Diagnosiscited by 9
  13. 2025Rainbow Noise: Stress-Testing Multimodal Harmful-Meme Detectors on LGBTQ Contentcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026GigaWorld-Policy: An Efficient Action-Centered World--Action Modelcited by 139
  2. 2026Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-offcited by 13
  3. 2026ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generationcited by 4
  4. 2026Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elitecited by 1
  5. 2026Quantifying Prior Dominance in RAG Systemscited by 0
  6. 2026Scope3Trace: Evidence-Based Identification and Extraction of Scope 3 GHG Emissions from Sustainability Reportscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Conflict-Aware Soft Prompting for Retrieval-Augmented Generation
  2. 2026Trust or Abstain? A Self-Aware RAG Approach
  3. 2025Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation
  4. 2025FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation
  5. 2025Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Robust Response Generation in the Wild
  6. 2026EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning
  7. 2025DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs
  8. 2026Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method