Large Language Models (LLMs), including GPT-3.5, LLaMA, and PaLM, seem to be knowledgeable and able to adapt to many tasks. However, we still cannot completely trust their answers, since LLMs suffer from \textbf{hallucination}\textemdash fabricating non-existent facts, deceiving users with or without their awareness. However, the reasons for their existence and pervasiveness remain unclear. In this paper, we demonstrate that nonsensical prompts composed of random tokens can also elicit the LLMs to respond with hallucinations. Moreover, we provide both theoretical and experimental evidence that transformers can be manipulated to produce specific pre-define tokens by perturbing its input sequence. This phenomenon forces us to revisit that \emph{hallucination may be another view of adversarial examples}, and it shares similar characteristics with conventional adversarial examples as a basic property of LLMs. Therefore, we formalize an automatic hallucination triggering method as the \textit{hallucination attack} in an adversarial way. Finally, we explore the basic properties of attacked adversarial prompts and propose a simple yet effective defense strategy. Our code is released on GitHub\footnote{https://github.com/PKU-YuanGroup/Hallucination-Attack}.
Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Electronic Science Research Institute of China Electronics（中国电子科技集团电子科学研究院）
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
Xi’an Jiaotong University（西安交通大学）；National Engineering Laboratory for Big Data Analytics（国家大数据分析工程实验室）；Nanyang Technological University（南洋理工大学）；Lenovo Research（联想研究院）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；Zhejiang University of Technology（浙江工业大学）
Invisible Ink, Visible Lies: How Production Watermarking Causes LLMs to Hallucinate
Nanjing University of Science and Technology（南京理工大学）；Anhui University of Technology（安徽工业大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Luxembourg（卢森堡大学）；CSIRO（澳大利亚联邦科学与工业研究组织）
Specula: Scaling formal specifications for autonomous model checking of system code
Nanjing University（南京大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft Research Asia（微软亚洲研究院）；University of British Columbia（不列颠哥伦比亚大学）
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；University of Oxford（牛津大学）；Council on Economic Policies（经济政策委员会）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Eawag: Swiss Federal Institute of Aquatic Science（瑞士联邦水科学与技术研究所）；Swiss Finance Institute (SFI)（瑞士金融研究所）
Inteligencia Artificial jurídica y el desafío de la veracidad: análisis de alucinaciones, optimización de RAG y principios para una integración responsable
From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models
University of Chinese Academy of Sciences（中国科学院大学）；Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能研究院，清华大学）；Gaoling School of Artificial Intelligence, Renmin University of China（人工智能学院，中国人民大学）；Kuaishou Technology Inc.（快手科技有限公司）；Shanghai Key Laboratory of Multi. Info. Processing, East China Normal University（多信息处理重点实验室，华东师范大学）
Bridging the Knowledge-Action Gap by Evaluating LLMs in Dynamic Dental Clinical Scenarios
The Second Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第二医院）；Peking University School and Hospital of Stomatology（北京大学口腔医学院（口腔医院））；Medlinker Intelligent and Digital Technology Co., Ltd（医联智数科技有限公司）；The University of Queensland（昆士兰大学）；Beijing Xicheng District Health Care Hospital for Mothers and Children（北京市西城区妇幼保健院）
Automatic Minds: Cognitive Parallels Between Hypnotic States and Large Language Model Processing
IRCCS Istituto Auxologico Italiano（意大利辅助医学研究所）；Università Cattolica del Sacro Cuore（圣心天主教大学）；Virtual Reality Medical Center（虚拟现实医学中心）；Virtual Reality Medical Institute（虚拟现实医学研究所）；University of Milano Bicocca（米兰比可卡大学）
Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving
Florida International University（佛罗里达国际大学）；SRI International（SRI国际（斯坦福国际研究院））；United States Military Academy（美国军事学院（西点军校））；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Central Florida（中佛罗里达大学）；Vanderbilt University（范德堡大学）
Dynamic Planning for LLM-based Graphical User Interface Automation
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
