Large Language Models (LLMs) have revolutionized numerous applications, making them an integral part of our digital ecosystem. However, their reliability becomes critical, especially when these models are exposed to misinformation. We primarily analyze the susceptibility of state-of-the-art LLMs to factual inaccuracies when they encounter false information in a QnA scenario, an issue that can lead to a phenomenon we refer to as *knowledge drift*, which significantly undermines the trustworthiness of these models. We evaluate the factuality and the uncertainty of the models' responses relying on Entropy, Perplexity, and Token Probability metrics. Our experiments reveal that an LLM's uncertainty can increase up to 56.6% when the question is answered incorrectly due to the exposure to false information. At the same time, repeated exposure to the same false information can decrease the models uncertainty again (-52.8% w.r.t. the answers on the untainted prompts), potentially manipulating the underlying model's beliefs and introducing a drift from its original knowledge. These findings provide insights into LLMs' robustness and vulnerability to adversarial inputs, paving the way for developing more reliable LLM applications across various domains. The code is available at https://github.com/afastowski/knowledge_drift.
Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation
Mass General Brigham（麻省总医院布莱根医疗系统）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Boston Children’s Hospital（波士顿儿童医院）；Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Virginia（弗吉尼亚大学）；Maastricht University（马斯特里赫特大学）
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information
The University of Manchester（曼彻斯特大学）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；The University of Melbourne（墨尔本大学）；Carnegie Mellon University（卡内基梅隆大学）
On the Risk of Misinformation Pollution with Large Language Models
National University of Singapore（新加坡国立大学）；University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；University of Waterloo（滑铁卢大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Zhejiang University（浙江大学）
CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking
School of Cyber Science and Engineering, Sichuan University, China（四川大学网络科学与工程学院）；College of Computer Science, Sichuan University, China（四川大学计算机学院）；Institute of Data Science, National University of Singapore, Singapore（新加坡国立大学数据科学研究所）
Leveraging Knowledge Graphs and LLMs for Structured Generation of Misinformation
Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna（机器人与人工智能卓越部门，圣安娜高等学院）；Institute of Informatics and Telematics, National Research Council of Italy（信息与电信研究所，意大利国家研究理事会）；Sapienza Università di Roma（罗马萨皮恩扎大学）
VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation
The University of Melbourne（墨尔本大学）；Deakin University（迪肯大学）；Singapore Management University（新加坡管理大学）；City University of Hong Kong（香港城市大学）；Queensland University of Technology（昆士兰理工大学）；Fudan University（复旦大学）
A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；Ant Group, Alibaba（蚂蚁集团，阿里巴巴）；Northeast Forest University（东北林业大学）
Optimizing Social Media Annotation of HPV Vaccine Skepticism and Misinformation Using Large Language Models: An Experimental Evaluation of In-Context Learning and Fine-Tuning Stance Detection Across Multiple Models
School of Journalism and Mass Communication, University of Wisconsin–Madison（威斯康星大学麦迪逊分校新闻与大众传播学院）；Department of Electrical & Computer Engineering, University of Wisconsin–Madison（威斯康星大学麦迪逊分校电气与计算机工程系）；Department of Computer Science, University of Wisconsin–Madison（威斯康星大学麦迪逊分校计算机科学系）；Department of Psychology, University of Wisconsin–Madison（威斯康星大学麦迪逊分校心理学系）；School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院交互计算学院）；Institute of People and Technology, Georgia Institute of Technology（佐治亚理工学院人与技术研究所）
Customized large language models can outperform Community Notes in correcting misinformation
Paul G. Allen School of Computer Science and Engineering, University of Washington（保罗·G·阿伦计算机科学与工程学院，华盛顿大学）；Computer Science Department, Boise State University（计算机科学系，博伊西州立大学）；Microsoft Corporation（微软公司）
Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models
