A knowledge system S describing a part of real world does in general not contain complete information. Reasoning with incomplete information is prone to errors since any belief derived from S may be false in the present state of the world. A false belief may suggest wrong decisions and lead to harmful actions. So an important goal is to make false beliefs as unlikely as possible. This work introduces the notions of "typical atoms" and "typical models", and shows that reasoning with typical models minimizes the expected number of false beliefs over all ways of using incomplete information. Various properties of typical models are studied, in particular, correctness and stability of beliefs suggested by typical models, and their connection to oblivious reasoning.
Beyond the Shadows of Plato's Cave: Evaluating False Memory in Autonomous Agents via Counterfactual Reasoning
Sydney AI Centre, The University of Sydney（悉尼大学悉尼人工智能中心）；Australian Institute for Machine Learning, Adelaide University（阿德莱德大学澳大利亚机器学习研究所）；Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；Wuhan University（武汉大学）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学院）
Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation
Massachusetts Institute of Technology（麻省理工学院）；MIT Media Lab（麻省理工学院媒体实验室）；Kellogg School of Management（凯洛格管理学院）；Northwestern University（西北大学）；Institute for Human-Centered AI（以人为中心的人工智能研究所）；Stanford University（斯坦福大学）
Fact-checking information from large language models can decrease headline discernment
Observatory on Social Media, Indiana University（社会媒体观察所，印第安纳大学）；FSI Cyber Policy Center, Stanford University（斯坦福大学FSI网络政策中心）；Network Science Institute, Northeastern University（东北大学网络科学研究所）
Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
Bonn-Aachen International Center for Information Technology (b-it)（波恩-埃森国际信息科技中心）；University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；Research Center Trustworthy Data Science and Security (RC-Trust)（可信数据科学与安全研究中心）；University of Duisburg-Essen（埃森-杜伊斯堡大学）
本研究通过扰动任务和链式推理提示探讨大语言模型的理论 of mind能力，发现其鲁棒性受扰动影响显著，CoT提示虽提升整体表现，但对某些扰动类别却降低准确性。
