Hallucination in Language Models

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13 papers in the last four weeks, up 160% on the four weeks before. 0.1% of all new papers.

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Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time
Jun 1, 2026Mingkuan Zhao, Yide Gao, Wentao Hu +6Contextual Faithfulness Hallucination in Language Models
Xi’an Jiaotong University · University of Science and Technology of China · Tongji University +1
Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs
May 30, 2026Aizierjiang AiersilanHallucination in Language Models LLM Hallucination Detection
University of Macau
BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali
May 29, 2026Shefayat E Shams Adib, Ahmed Alfey Sani, Ekramul Alam Esham +3Multilingual Language Model Evaluation Language Model Error Detection
Department of Computer Science and Engineering, Islamic University of Technology · Department of Computer Science and Engineering, University of California
Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA
May 29, 2026Hao Chen, Xing Tang, Qirui Liu +6Retrieval-Augmented Generation Hallucination in Language Models
Shenzhen Technology University FiT, Tencent Shenzhen, China · South China University of Technology Guangzhou, China · The Hong Kong University of Science and Technology Hong Kong SAR, China +2
Mitigating Hallucination in Vision-Language Models through Barrier-Regulated Adaptive Closed-form Steering
May 28, 2026Soumyadeep Jana, Pulkit Mittal, Sanasam Ranbir SinghHallucination in Language Models Large Vision-Language Models
Indian Institute of Technology Guwahati, India
Social Reasoning in Machines: Investigating Collective Truth-Seeking Dynamics in Large Language Model Debate
May 28, 2026Tom PecherLLM Evaluation Hallucination in Language Models
Department of Computer Science University of Bath
ReactBench: A Cause-Driven Benchmark for Multimodal Hallucination via Systematic Evaluation
May 28, 2026Shizhe Zhou, Bohan Jia, Kai Wu +4VLM Robustness Hallucination Detection
East China Normal University
K-FinHallu: A Hallucination Detection Benchmark for Multi-Turn RAG in Korean Finance
May 28, 2026Eunbyeol Cho, Yunseung Lee, Mirae Kim +3Retrieval-Augmented Generation Hallucination in Language Models
KAIST AI · Financial Tech Lab, KakaoBank Corp.
Harmless Yet Harmful: Neutral Prompting Attacks for Stealthy Hallucination Steering in Agent Skills
May 28, 2026Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang +1Adversarial Prompt Generation AI Coding Agents
Department of Computer Science National Yang Ming Chiao Tung University · Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University · School of Computing Institute of Science Tokyo
Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
May 27, 2026Diego Gosmar, Deborah A. DahlAI Agent Reliability Multi-Agent LLM Systems
Head of AI, Tesisquare · Member, Open Voice Interoperability Initiative · Linux Foundation AI & Data +4
Hallucination Detection-Guided Preference Optimization for Clinical Summarization
May 27, 2026Shamanth Kuthpadi Seethakantha, Dung Ngoc Thai, Vara Prasad Gudi +6LLM Self-Refinement LLM Hallucination Mitigation
1Manning College of Information and Computer Sciences · 2Ensemble HP · 3Columbia University +1
Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
May 27, 2026Mattia J. Villani, Pranav Deshpande, Akshay Seshadri +2Language Model Error Detection Language Model Generation Evaluation
Global Technology Applied Research, JPMorganChase, New York, NY 10001, USA
Risk-aware Selective Prompting for Hallucination Mitigation in Large Vision-Language Models
May 27, 2026Yuang Huang, Yafeng Zhang, Yu ZilanVision-Language Models Hallucination in Language Models
Shanghai Jiao Tong University · iFLYTEK · Tsinghua University
Rethinking Visual Neglect: Steering via Context-Preference for MLLM Hallucination Mitigation
May 27, 2026Jingwen Wu, Xijun Zhang, Ge SongLLM Hallucination Mitigation Hallucination in Language Models
School of Computer and Electronic Information, Nanjing Normal University, China
Reasoning Matters: Mitigate Hallucination in Multimodal Large Reasoning Models via Reasoning-Conditioned Preference Optimization
May 27, 2026Jiawei Kong, Hao Fang, Shunxiang Liao +5Multimodal Grounding Direct Preference Optimization
Tsinghua Shenzhen International Graduate School, Tsinghua University · Harbin Institute of Technology, Shenzhen
Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction
May 26, 2026Joan Vendrell Gallart, Solmaz Kia, Russell Bent +1LLM Hallucination Mitigation Hallucination in Language Models
Department of Mechanical and Aerospace University of California Irvine Irvine, CA 92617-4322, USA · T-5 Los Alamos National Laboratory Los Alamos, NM 88220, USA · CAI-4 Los Alamos National Laboratory Los Alamos, NM 88220, USA
Can Hallucinations Be Useful? Solving Multi-Hop Questions With SLMs By Chaining System-I/II Reasoning
May 26, 2026Saptarshi Sengupta, Suhang WangMulti-Hop QA LLM Hallucination Mitigation
The Pennsylvania State University
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
Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination
May 26, 2026Yedidia Agnimo, Anna Korba, Annabelle Blangero +2LLM Evaluation Hallucination in Language Models
Ekimetrics · Centre Inria de l’Université Grenoble Alpes · CREST, ENSAE +1
Innovation: An Almost Characterization of Hallucination
May 26, 2026Nishant P. Das, Piyush SrivastavaHallucination in Language Models Language Model Calibration
School of Technology and Computer Science, Tata Institute of Fundamental Research, Mumbai, Maharashtra - 400 005, India.
Automatic Layer Selection for Hallucination Detection
May 25, 2026Xinpeng Wang, William X. Cao, Andrew Gordon Wilson +1Hallucination in Language Models LLM Hallucination Detection
University of Virginia · 2Walrus Security · 3New York University.
Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations
May 25, 2026Shanghao Li, Jinda Han, Yibo Wang +5LLM Grounding LLM Interpretability
University of Illinois Chicago · University of Illinois Urbana-Champaign
Adversarial Orthogonal Disentanglement for LVLM Hallucination Mitigation
May 25, 2026Ruoxi Cheng, Haoxuan Ma, Zhengfei Hai +6Adversarial Representation Learning Vision-Language Models
Fudan University · Tencent · Nanjing University +3
Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
May 24, 2026Yuanzhi Xu, Qian Gao, Jun Fan +4VLM Robustness Vision-Language Models
Qilu University of Technology (Shandong Academy of Sciences) Jinan, China · China Telecom Digital Intelligence Technology Co, Ltd Jinan, China · Shenyang Aerospace University Shenyang, China +1
MultiHaluDet: Multilingual Hallucination Detection via LLM Hidden State Probing
May 24, 2026Riasad Alvi, Nurul Labib Sayeedi, Md. Faiyaz Abdullah SayeediHallucination Detection Hallucination in Language Models
United International University · BRAC University
Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
May 23, 2026Quanjiang Li, Zhiming Liu, Wei Luo +2LLM Hallucination Mitigation Hallucination in Language Models
National University of Defense Technology, China · Harbin Institute of Technology (Shenzhen), China · Xi’an Jiaotong University, China.
Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows
May 22, 2026Harshada Badave, Santosh Borse, Andrea Gomez +6AI Agent Reliability AI Agent Auditing
1IBM · 2Columbia University
CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs
May 22, 2026Xiaoyi Huang, Kejia Zhang, Zhiming LuoHallucination Detection in VLMs Efficient VLM Inference
Institute of Artificial Intelligence, Xiamen University · Department of Artificial Intelligence, Xiamen University
Graph Alignment Topology as an Inductive Bias for Grounding Detection
May 21, 2026Paul Landes, Pranav Herur, Adam Cross +1LLM Grounding Hallucination in Language Models
†Department of Pediatrics, University of Illinois College of Medicine Peoria · ♣University of Illinois Urbana-Champaign · ♠Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign +1
Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer
May 21, 2026Jewon Yeom, Jaewon Sok, Heejun Kim +3LLM Interpretability Hallucination in Language Models
Graduate School of Data Science, Seoul National University · Department of Rural Systems Engineering, Seoul National University · Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology +1

Jun 1, 2026, cs.CLJ/K move · Enter open · S save

Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time

Mingkuan Zhao, Yide Gao, Wentao Hu, Suquan Chen+5

Xi’an Jiaotong University · University of Science and Technology of China · Tongji University+1

Contextual Faithfulness Hallucination in Language Models Causal Interventions in Language Models Inference-Time Intervention

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Large Language Models (LLMs) frequently exhibit "contextual disregard" when faced with input evidence that conflicts with their internal parametric memory, leading to persistent factual hallucinations. Existing mitigation strategies primarily rely on suppressing specific neuron activations or employing computationally expensive contrastive decoding mechanisms, which often result in increased perplexity or significantly elevated inference latency. To address these limitations, we propose Resonant Context Anchoring (RCA), a lightweight inference-time intervention method grounded in the perspective of residual stream signal dynamics. RCA aims to resolve the signal attenuation of external evidence during its propagation through deep networks. The core mechanism involves the orthogonal decoupling of routing logic and information magnitude within the self-attention module. By utilizing raw pre-softmax attention scores as an instantaneous metric of semantic alignment, we construct a dynamic gain field via non-linear rectification to selectively amplify the norms of value vectors corresponding to context tokens, without altering the attention probability distribution. This mechanism effectively elevates the signal-to-noise ratio (SNR) of input evidence within the residual stream mixture, thereby robustly anchoring the generation trajectory to the truthful context during inference. Extensive experiments on the Llama-3 model series demonstrate that RCA significantly improves contextual faithfulness across multiple factual consistency and strong knowledge-conflict tasks, effectively suppressing parametric hallucinations. Furthermore, results confirm that as a training-free and computationally negligible plug-and-play module, RCA achieves a Pareto improvement in faithfulness and fluency while maintaining the model's general language understanding capabilities.

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