The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Nevertheless, LLMs are prone to hallucination, generating plausible yet nonfactual content. This phenomenon raises significant concerns over the reliability of LLMs in real-world information retrieval (IR) systems and has attracted intensive research to detect and mitigate such hallucinations. Given the open-ended general-purpose attributes inherent to LLMs, LLM hallucinations present distinct challenges that diverge from prior task-specific models. This divergence highlights the urgency for a nuanced understanding and comprehensive overview of recent advances in LLM hallucinations. In this survey, we begin with an innovative taxonomy of hallucination in the era of LLM and then delve into the factors contributing to hallucinations. Subsequently, we present a thorough overview of hallucination detection methods and benchmarks. Our discussion then transfers to representative methodologies for mitigating LLM hallucinations. Additionally, we delve into the current limitations faced by retrieval-augmented LLMs in combating hallucinations, offering insights for developing more robust IR systems. Finally, we highlight the promising research directions on LLM hallucinations, including hallucination in large vision-language models and understanding of knowledge boundaries in LLM hallucinations.
Is Imagination Derived from Hallucination? A Cross-Taxonomy Evaluation of Imagination and Hallucination in Large Language Models
City University of Hong Kong（香港城市大学）；Northwestern Polytechnical University（西北工业大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术大学人工智能研究院）
AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agents
Beijing University of Posts and Telecommunications（北京邮电大学）；Department of Computer Science & Technology, Tsinghua University（清华大学计算机科学与技术系）；University of California, Santa Barbara（加州大学圣芭芭拉分校）；Center for Research on Intelligent Perception and Computing, NLPR, CASIA（智能感知与计算研究中心，国家工程实验室）
Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations
Johns Hopkins University（约翰斯·霍普金斯大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of Maryland, Carey School of Law（马里兰大学凯里法学院）
Unified Hallucination Fuzzing for Multimodal Large Language Models
National University of Singapore（新加坡国立大学）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Renmin University of China（中国人民大学）；Zhejiang University（浙江大学）；Hupan Lab（湖畔实验室）；University of California, Berkeley（加州大学伯克利分校）；Rochester Institute of Technology（罗切斯特理工学院）；The Hong Kong University of Science and Technology（香港科技大学）
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
Indian Institute of Technology Kharagpur（印度理工学院Khargapur分校）；Rochester Institute of Technology（罗切斯特理工学院）；Researcher, Fatima Fellowship（研究员，Fatima fellowship）；AI Institute, University of South Carolina（人工智能研究所，南卡罗来纳大学）；Amazon GenAI（亚马逊生成人工智能）；Stanford University（斯坦福大学）；James Silberrad Brown Center for AI, San Diego State University（詹姆斯·西伯拉德·布朗人工智能中心，圣地亚哥州立大学）
When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
School of Law, China University of Political Science and Law（中国政法大学法学院）；Zhejiang University of Finance and Economics Dongfang College（浙江财经大学东洋学院）；School of Information Management for Law, China University of Political Science and Law（中国政法大学信息管理法学院）；Department of Artificial Intelligence, Chung-Ang University（成均馆大学人工智能系）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
Hallucinations are inevitable but can be made statistically negligible
Department of Mathematics（数学系）；The University of Hong Kong（香港大学）；Department of Informatics（信息学院）；King’s College London（伦敦国王学院）；Division of Natural and Applied Sciences（自然科学与应用科学系）；Duke Kunshan University（杜克大学昆山分校）；School of Computer Science（计算机科学学院）
