The recent proliferation of research into transformer based natural language processing has led to a number of studies which attempt to detect the presence of human-like cognitive behavior in the models. We contend that, as is true of human psychology, the investigation of cognitive behavior in language models must be conducted in an appropriate population of an appropriate size for the results to be meaningful. We leverage work in uncertainty estimation in a novel approach to efficiently construct experimental populations. The resultant tool, PopulationLM, has been made open source. We provide theoretical grounding in the uncertainty estimation literature and motivation from current cognitive work regarding language models. We discuss the methodological lessons from other scientific communities and attempt to demonstrate their application to two artificial population studies. Through population based experimentation we find that language models exhibit behavior consistent with typicality effects among categories highly represented in training. However, we find that language models don't tend to exhibit structural priming effects. Generally, our results show that single models tend to over estimate the presence of cognitive behaviors in neural models.
Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench
The Chinese University of Hong Kong（香港中文大学）；Tianjin Medical University（天津医科大学）；Tencent AI Lab（腾讯AI实验室）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects
Wuhan University（武汉大学）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所-ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共援助医院）；Beijing University of Technology（北京工业大学）
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑科学院）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公立医院集团）；Hôpital de la Pitié Salpêtrière（萨尔佩特里埃医院）；Beijing University of Technology（北京工业大学）；Wuhan University（武汉大学）；Wuhan Mental Health Center（武汉市精神卫生中心）；Wuhan Hospital for Psychotherapy（武汉市心理医院）
Deep Learning and Large Language Models for Audio and Text Analysis in Predicting Suicidal Acts in Chinese Psychological Support Hotlines
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital（北京回龙观医院北京自杀研究与预防中心）；WHO Collaborating Center for Research and Training in Suicide Prevention（世界卫生组织自杀预防研究与培训合作中心）；Peking University Huilongguan Clinical Medical School（北京大学回龙观临床医学院）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所（ICM））；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié Salpêtrière（萨尔皮特里耶医院）
Hong Kong Polytechnic University（香港理工大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Bologna（博洛尼亚大学）；Sichuan University（四川大学）；Microsoft（微软公司）
PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health
Stony Brook University（石溪大学）；New York University（纽约大学）；University of Melbourne（墨尔本大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Pennsylvania（宾夕法尼亚大学）；Stanford University（斯坦福大学）
