Large Language Models (LLMs) can make up answers that are not real, and this is known as hallucination. This research aims to see if, how, and to what extent LLMs are aware of hallucination. More specifically, we check whether and how an LLM reacts differently in its hidden states when it answers a question right versus when it hallucinates. To do this, we introduce an experimental framework which allows examining LLM's hidden states in different hallucination situations. Building upon this framework, we conduct a series of experiments with language models in the LLaMA family (Touvron et al., 2023). Our empirical findings suggest that LLMs react differently when processing a genuine response versus a fabricated one. We then apply various model interpretation techniques to help understand and explain the findings better. Moreover, informed by the empirical observations, we show great potential of using the guidance derived from LLM's hidden representation space to mitigate hallucination. We believe this work provides insights into how LLMs produce hallucinated answers and how to make them occur less often.
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；Stanford University（斯坦福大学）；Penn State University（宾夕法尼亚州立大学）；Hong Kong University of Science and Technology（香港科技大学）
Faithful Activation Verbalization: Reducing Hallucinations in LLM Representation Interpretation
New Jersey Institute of Technology（新泽西理工学院）；Shanghai AI Laboratory（上海人工智能实验室）；Xi’an JiaoTong University（西安交通大学）；Chinese Univerity of Hongkong, Shenzhen（香港中文大学（深圳））
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；State Key Laboratory of Computer Science（计算机科学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer
Graduate School of Data Science（数据科学研究生院）；Department of Rural Systems Engineering（农村系统工程系）；Electrical Engineering and Computer Science（电子工程与计算机科学）；Department of Aerospace Engineering（航空航天工程系）
AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models
School of Artificial Intelligence, Beihang University, China（北京航空航天大学人工智能学院）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, China（北京航空航天大学未来区块链与隐私计算先进创新中心）
Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge
Beijing Institute of Technology, China（北京理工大学）；Beijing University of Posts and Telecommunications, China（北京邮电大学）；Meituan, China（美团）；The Open University, UK（开放大学）
Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer
Technion – Israel Institute of Technology（技术ion-以色列理工学院）；University of Oxford and WhiteBox（牛津大学和WhiteBox）；School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）
CCNU at SemEval-2025 Task 3: Leveraging Internal and External Knowledge of Large Language Models for Multilingual Hallucination Annotation
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning（湖北省人工智能与智能学习重点实验室）；National Language Resources Monitoring and Research Center for Network Media（网络媒体语言资源监测与研究中心）；School of Computer Science, Central China Normal University（中央财经大学计算机学院）
On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning
Beijing University of Posts and Telecommunications（北京邮电大学）；Huazhong University of Science and Technology（华中科技大学）；Shandong University（山东大学）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences（神经网络计划、人工智能研究所、中国科学院自动化研究所）
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China（中国科学院软件研究所信息处理实验室，北京，中国）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）；Xiaohongshu Inc（小红书公司）
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
College of AI, Tsinghua University（清华大学人工智能学院）；Department of Electronic Engineering, BNRist, Tsinghua University（清华大学电子工程系）；School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子信息学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
