Knowledge Conflicts in Language Models

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13 papers in the last four weeks, up 18% on the four weeks before. 0.1% of all new papers.

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The Future of Facts: Tracing the Factual Generation-Verification Gap
May 26, 2026Tim R. Davidson, Anja Surina, Caglar GulcehreKnowledge Conflicts in Language Models Factual Knowledge in Language Models
EPFL
Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs
May 26, 2026Zhe Yu, Wenpeng Xing, Chen Ye +4Language Model Safety Evaluation Knowledge Conflicts in Language Models
Binjiang Institute of Zhejiang University · Zhejiang University · Hangzhou Dianzi University +1
On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning
May 26, 2026Xiaotian Ye, Xiaohan Wang, Mengqi Zhang +1Knowledge Conflicts in Language Models Hallucination in Language Models
Beijing University of Posts and Telecommunications · Huazhong University of Science and Technology · Shandong University +1
Priors Persist Through Suppression: A Stroop Paradigm for Lexical Override
May 25, 2026Han-yu WangKnowledge Conflicts in Language Models LLM Interpretability
The University of Hong Kong
Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models
May 22, 2026Maikel Yelandi Leyva-Vázquez, Florentin SmarandacheLLM Evaluation Knowledge Conflicts in Language Models
Universidad Bolivariana del Ecuador, Coordinación Académica de Posgrado, Durán, Ecuador · Universidad de Guayaquil, Guayaquil, Ecuador · Universidad Bernardo O’Higgins, Santiago, Chile +1
ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
May 17, 2026Chenyu Wang, Yueyuan Li, Yingmin Liu +1Retrieval-Augmented Generation Knowledge Conflicts in Language Models
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China · College of Environment and Ecology, Taiyuan University of Technology, Taiyuan, China · Zhejiang University, Hangzhou, China
Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict
May 14, 2026Yihang Chen, Pin Qian, Su Wang +4Retrieval-Augmented Generation Knowledge Conflicts in Language Models
1Georgia Institute of Technology · 2Carnegie Mellon University · University of California San Diego
When Evidence Conflicts: Uncertainty and Order Effects in Retrieval-Augmented Biomedical Question Answering
May 13, 2026Yikun Han, Mengfei Lan, Halil KilicogluSelective Prediction Healthcare
University of Illinois Urbana-Champaign
Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding
May 12, 2026Yigeng Zhou, Wu Li, Yifan Lu +6Language Model Decoding Knowledge Conflicts in Language Models
Harbin Institute of Technology, Shenzhen, 518055, China · Beijing Academy of Artificial Intelligence, China · Nanyang Technological University, Singapore
To Whom Do Language Models Align? Measuring Principal Hierarchies Under High-Stakes Competing Demands
May 12, 2026Fangyi Yu, Nabeel Seedat, Jonathan Richard Schwarz +1LLM Alignment Language Model Safety Evaluation
Thomson Reuters Foundational Research, 5 Canada Square, London · Imperial College London · University of Oxford
Three Regimes of Context-Parametric Conflict: A Predictive Framework and Empirical Validation
May 12, 2026Pruthvinath Jeripity VenkataLLM Grounding Knowledge Conflicts in Language Models
Independent Researcher
Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing
May 11, 2026Qinghua Mao, Xi Lin, Jinze Gu +3Knowledge Editing Knowledge Conflicts in Language Models
School of Computer Science · Shanghai Jiao Tong University
Trust or Abstain? A Self-Aware RAG Approach
May 11, 2026Xi Zhu, Ziqi Wang, Kai Mei +5Retrieval-Augmented Generation Knowledge Conflicts in Language Models
Rutgers University · University of Illinois Urbana-Champaign
Attractor Geometry of Transformer Memory: From Conflict Arbitration to Confident Hallucination
May 7, 2026Qiyao Liang, Risto Miikkulainen, Ila FieteMemorization in Language Models Knowledge Conflicts in Language Models
Massachusetts Institute of Technology · University of Texas Austin, Cognizant · Cognizant
Truth or Tribe: How In-group Favoritism Prioritize Facts in Persona Agents
May 2, 2026Shijun Lei, Hongyu Wang, Yunji Liang +3Knowledge Conflicts in Language Models Social Bias in Language Models
Northwestern Polytechnical University · Central University of Finance and Economics
In-Context Examples Suppress Scientific Knowledge Recall in LLMs
Apr 30, 2026Chaemin Jang, Woojin Park, Hyeok Yun +2Knowledge Conflicts in Language Models Scientific Reasoning
Korea Advanced Institute of Science and Technology · Shanghai Jiao Tong University
Quantifying Prior Dominance in RAG Systems
Apr 29, 2026Barak OrLLM Grounding Knowledge Conflicts in Language Models
ArtificialGate Ltd. · Google & Reichman Tech School · Technion – Israel Institute of Technology +1
Large language models eroding science understanding: an empirical study of malignment
Apr 28, 2026Harry Collins, Hartmut Grote, Paul Newbury +2LLM Evaluation Knowledge Conflicts in Language Models
i · ii · iii
Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models
Apr 28, 2026Li Ju, Junzhe Wang, Qi ZhangContextual Faithfulness Retrieval-Augmented Generation
College of Computer Science and Artificial Intelligence, Fudan University
Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors
Apr 28, 2026Long Zhang, Zi-bo Qin, Wei-neng ChenKnowledge Conflicts in Language Models LLM Auditing
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation
Apr 26, 2026Shuaizhi Cheng, Xiang Shi, Zhiwei Zhang +1Knowledge Conflicts in Language Models LLM Fine-Tuning
Harbin Institute of Technology · KigLand Machine Learning Lab · Imperial College London
How Large Language Models Balance Internal Knowledge with User and Document Assertions
Apr 24, 2026Shuowei Li, Haoxin Li, Wenda Chu +1LLM Sycophancy Knowledge Augmentation for Language Models
Santa Clara University · Nanyang Technological University · California Institute of Technology
DALM: A Domain-Algebraic Language Model via Three-Phase Structured Generation
Apr 17, 2026Chao LiConstrained Generative Modeling Knowledge Conflicts in Language Models
In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts
Mar 26, 2026Matthias Busch, Marius Tacke, Sviatlana V. Lamaka +4LLM Evaluation Knowledge Conflicts in Language Models
Institute for Continuum and Material Mechanics, Technical University of Hamburg, Eißendorfer Straße, 21073 Hamburg, Germany · Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Max-Planck-Straße, 21502 Geesthacht, GermanyJun · Institute of Surface Science, Helmholtz-Zentrum Hereon, Max-Planck-Straße, 215022026 Geesthacht, Germany +2
To Copy or Not to Copy: Copying Is Easier to Induce Than Recall
Jan 17, 2026Mehrdad Farahani, Franziska Penzkofer, Richard JohanssonKnowledge Conflicts in Language Models LLM Interpretability
1Chalmers University of Technology · University of Gothenburg
Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace Disentanglement
Nov 3, 2025Sekh Mainul Islam, Pepa Atanasova, Isabelle AugensteinDisentangled Representation Learning LLM Grounding
University of Copenhagen
The Chameleon Nature of LLMs: Quantifying Multi-Turn Stance Instability in Search-Enabled Language Models
Oct 19, 2025Shivam Ratnakar, Sanjay RaghavendraRetrieval-Augmented Generation LLM Evaluation
University of Southern California
When Tools Hurt LLM Reasoning: State-Dependent Belief Revision under External Evidence
Aug 21, 2025Yufeng Zhao, Junnan Liu, Hongwei Liu +4Belief Revision Knowledge Conflicts in Language Models
Shanghai Artificial Intelligence Laboratory · Department of Electronic Engineering, Tsinghua University · Beijing National Research Center for Information Science and Technology +2
Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts
Aug 2, 2025Xianda Zheng, Zijian Huang, Meng-Fen Chiang +4Knowledge Conflicts in Language Models
School of Computer Science, University of Auckland · Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University · School of Computing and Information Systems, Singapore Management University +1
ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
Feb 21, 2025Pengcheng Huang, Zhenghao Liu, Yukun Yan +8Retrieval-Augmented Generation Knowledge Conflicts in Language Models
School of Computer Science and Engineering, Northeastern University, China · Department of Computer Science and Technology, Institute for AI, Tsinghua University, China · Microsoft Research Asia, Beijing, China +1

May 26, 2026, cs.CLJ/K move · Enter open · S save

The Future of Facts: Tracing the Factual Generation-Verification Gap

Tim R. Davidson, Anja Surina, Caglar Gulcehre

EPFL

Knowledge Conflicts in Language Models Factual Knowledge in Language Models

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Language models are becoming the default interface to factual knowledge, yet they often verify outputs more reliably than they generate them. This generation-verification gap (GV-gap) underlies many recent advances in self-improvement and reasoning, but its dynamics on factual knowledge specifically remain poorly understood. We focus on the training mechanisms underlying factual GV-gaps, distinguishing them from their computational and aesthetic counterparts. We trace generation and verification capabilities through three training phases (acquisition, continual learning, and updating) across four open-source model families at two scales each. Three findings recur across models: (i) verification is consistently learned before generation; (ii) verification is more robust to continual learning than generation; and (iii) factual updates can leave models in a "multi-verse" state, simultaneously verifying both old and new answers as correct. Natural experiments on frontier models reproduce these dynamics at scale and reveal residual verification biases on well-covered facts.

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