The problem of determining if a military unit has correctly understood an order and is properly executing on it is one that has bedeviled military planners throughout history. The advent of advanced language models such as OpenAI's GPT-series offers new possibilities for addressing this problem. This paper presents a mechanism to harness the narrative output of large language models and produce diagrams or "maps" of the relationships that are latent in the weights of such models as the GPT-3. The resulting "Neural Narrative Maps" (NNMs), are intended to provide insight into the organization of information, opinion, and belief in the model, which in turn provide means to understand intent and response in the context of physical distance. This paper discusses the problem of mapping information spaces in general, and then presents a concrete implementation of this concept in the context of OpenAI's GPT-3 language model for determining if a subordinate is following a commander's intent in a high-risk situation. The subordinate's locations within the NNM allow a novel capability to evaluate the intent of the subordinate with respect to the commander. We show that is is possible not only to determine if they are nearby in narrative space, but also how they are oriented, and what "trajectory" they are on. Our results show that our method is able to produce high-quality maps, and demonstrate new ways of evaluating intent more generally.
Metareasoning constraints couple narratives, affect and cognition
Applied Computational Psychiatry Lab（应用计算精神病学实验室）；Max Planck UCL Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克UCL计算精神病学与衰老研究中心）；Queen Square Institute of Neurology and Mental Health（圣夸克广场神经病学与心理健康研究所）；Neuroscience Department（神经科学系）；Division of Psychiatry（精神病学系）
Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice
Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
Department of Computer Science, Columbia University（哥伦比亚大学计算机科学系）；Department of Applied Physics and Applied Mathematics, Columbia University（哥伦比亚大学应用物理与应用数学系）；Department of Psychology, Northwestern University（西北大学心理学系）
Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health
IBM T. J. Watson Research Center（IBM TJ沃森研究中心）；Morehouse School of Medicine（莫尔豪斯医学院）；Cleveland Clinic Foundation（克利夫兰诊所基金会）；Queen’s University（女王大学）；Red Hat（红帽公司）
Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Dongbei University of Finance and Economics（东北财经大学）
