Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions. Previous hallucination evaluation studies on LVLMs have identified hallucinations in terms of objects, attributes, and relations but overlooked complex hallucinations that create an entire narrative around a fictional entity. In this paper, we introduce a refined taxonomy of hallucinations, featuring a new category: Event Hallucination. We then utilize advanced LLMs to generate and filter fine grained hallucinatory data consisting of various types of hallucinations, with a particular focus on event hallucinations, laying the groundwork for integrating discriminative and generative evaluation methods within our universal evaluation framework. The proposed benchmark distinctively assesses LVLMs ability to tackle a broad spectrum of hallucinations, making it a reliable and comprehensive tool for gauging LVLMs efficacy in handling hallucinations. We will release our code and data.
ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；HKGAI under InnoHK（香港人工智能研究院（InnoHK框架下））；Hong Kong University of Science and Technology（香港科技大学）
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
Shenzhen Graduate School, Peking University（北京大学深圳研究生院）；International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Pengcheng Laboratory（鹏城实验室）；Hong Kong University of Science and Technology（香港科技大学）
The System Hallucination Scale (SHS): A Minimal yet Effective Human-Centered Instrument for Evaluating Hallucination-Related Behavior in Large Language Models
Machine Learning and Information Science Group, Medical University of Graz（格拉茨医科大学机器学习与信息科学组）；Human Machine Mind Cooperation, Graz, Austria（格拉茨人类机智合作中心）；MIDATA Cooperative, Zurich, Switzerland（苏黎世MIDATA合作组织）；Human-Centered AI Lab, BOKU University Vienna（维也纳BOKU大学人本AI实验室）
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Computing, Dublin City University（都柏林城市大学计算机学院）；School of Public Health, Wuhan University（武汉大学公共卫生学院）；Insight Centre for Data Analytics, University of Galway（Galway大学数据分析研究中心）；Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据分析研究中心）
HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence, Beihang University（北京未来区块链与隐私计算先进研究院，北京航空航天大学人工智能学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Renmin University of China（中国人民大学）
HausaNLP at SemEval-2025 Task 3: Towards a Fine-Grained Model-Aware Hallucination Detection
HausaNLP（豪萨语自然语言处理研究组）；University of Abuja（阿布贾大学）；Bayero University Kano（卡诺贝罗大学）；University of Pretoria（比勒陀利亚大学）；Imperial College London（伦敦帝国学院）；Northeastern University（东北大学）
An Audit on the Perspectives and Challenges of Hallucinations in NLP
College of Information Sciences and Technology, Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）；School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院交互计算学院）；Department of Computer Science & Engineering, Pennsylvania State University（宾夕法尼亚州立大学计算机科学与工程系）；Adobe Research（奥多比研究中心）
Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明负责任的人工智能与数据分析实验室）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹大学）；University of Electronic Science and Technology of China（中国电子科学技术大学）；University of Copenhagen（哥本哈根大学）
Evaluation and Analysis of Hallucination in Large Vision-Language Models
Beijing Jiaotong University（北京交通大学）；Xi’an Jiaotong University（西安交通大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shandong University（山东大学）
