LLM Hallucination Mitigation

LLM: Large Language Model

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9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

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RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
Apr 17, 2026Fabian Ridder, Laurin Lessel, Malte SchillingRetrieval-Augmented Generation LLM Hallucination Mitigation
Computer Science Department, University of Münster, Münster, Germany
Why Fine-Tuning Encourages Hallucinations and How to Fix It
Apr 16, 2026Guy Kaplan, Zorik Gekhman, Zhen Zhu +5Supervised Fine-Tuning Continual Learning for LLMs
♡Hebrew University of Jerusalem · ♣Technion – Israel Institute of Technology · ♢University of Illinois Urbana-Champaign +1
Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias
Apr 3, 2026Shuai Wu, Xue Li, Yanna Feng +3LLM Hallucination Mitigation Multi-Agent LLMs
Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study
Feb 19, 2026Dylan Bouchard, Mohit Singh Chauhan, Viren Bajaj +1LLM Hallucination Mitigation Hallucination in Language Models
CVS Health, Wellesley, MA
FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning
Feb 15, 2026Jinzi Zou, Bolin Wang, Shuo Zhang +2LLM Hallucination Mitigation Conversational Agents
MoE KLINNS Lab, Xi’an Jiaotong University, Xi’an 710049, P. R. China · China Mobile Group Shaanxi Co., Ltd.
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
Jan 20, 2026Qianli Ma, Chang Guo, Zhiheng Tian +4Multi-Agent LLM Systems LLM Hallucination Mitigation
AutoLab, School of Artificial Intelligence, Shanghai Jiao Tong University
Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs
Jan 5, 2026Amirali Ebrahimzadeh, Seyyed M. SaliliLong-Context Retrieval LLM Hallucination Mitigation
Department of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA · Independent Researcher, Ann Arbor, MI 48106, USA
Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation
Nov 14, 2025Mohamad Amin Mohamadi, Tianhao Wang, Zhiyuan LiRL for Language Models LLM Hallucination Mitigation
Toyota Technological Institute at Chicago · University of California, San Diego
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
Oct 28, 2025Yihan Li, Xiyuan Fu, Ghanshyam Verma +2Retrieval-Augmented Generation LLM Hallucination Mitigation
Electronic Information School, Wuhan University, Wuhan, China · School of Computing, Dublin City University, Dublin, Ireland · School of Public Health, Wuhan University, Wuhan, China +2
Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration
Sep 25, 2025Dongkyu Cho, Miao Zhang, Rumi ChunaraSynthetic Data Augmentation Data Augmentation
Department of Computer Science, New York University, New York, NY, United States · Department of Biostatistics, School of Global Public Health, New York University, New York, NY, United States
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Sep 17, 2025Edward Phillips, Sean Wu, Soheila Molaei +3Uncertainty Quantification LLM Hallucination Mitigation
Department of Engineering Science, University of Oxford · GlaxoSmithKline · Oxford Suzhou Centre for Advanced Research
SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision
Aug 5, 2025Zhaoxu Li, Chenqi Kong, Yi Yu +6VLM Evaluation LLM Hallucination Mitigation
ROSE Lab, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · ROSE Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · City University of Hong Kong, Hong Kong SAR +3
Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Jul 21, 2025Siyuan Liu, Wenjing Liu, Zhiwei Xu +3LLM Hallucination Mitigation Hallucination in Language Models
College of Computer Science, Nankai University · College of intelligent Science and Technology, Inner Mongolia University of Technology · Institute of Computing Technology, Chinese Academy of Sciences +3
Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity
May 23, 2025Shiyu Ni, Keping Bi, Jiafeng Guo +1LLM Hallucination Mitigation Confidence Estimation in Language Models
CAS Key Lab of Network Data Science and Technology, ICT, CAS · State Key Laboratory of AI Safety · University of Chinese Academy of Sciences
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
Mar 3, 2025Tin Nguyen, Logan Bolton, Mohammad Reza Taesiri +2LLM Grounding LLM Answer Verification
Auburn University · University of Alberta

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RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration

Fabian Ridder, Laurin Lessel, Malte Schilling

Computer Science Department, University of Münster, Münster, Germany

Retrieval-Augmented Generation LLM Hallucination Mitigation Hallucination in Language Models Language Modeling LLM Hallucination Detection RAG Robustness

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Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or domain-specific knowledge. Nonetheless, current models still produce closed-domain hallucinations and generate content that is unsupported by the retrieved context. Current detection approaches typically treat hallucination as a post-hoc problem, relying on black-box consistency checks or probes over frozen internal representations. In this work, we demonstrate that hallucination detection based on internal state representation can also serve as a direct training signal. We introduce RAGognize, a dataset of naturally occurring closed-domain hallucinations with token-level annotations, and RAGognizer, a hallucination-aware fine-tuning approach that integrates a lightweight detection head into an LLM, allowing for the joint optimization of language modeling and hallucination detection. This joint objective forces the model to improve the separability of its internal states regarding hallucinations while simultaneously learning to generate well-formed and meaningful responses. Across multiple benchmarks, RAGognizer achieves state-of-the-art token-level hallucination detection while substantially reducing hallucination rates during generation, without degrading language quality or relevance.

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