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arXiv 2307.03952cs.CL
ChatGPT 是一个好的性格识别器吗？一项初步研究
Is ChatGPT a Good Personality Recognizer? A Preliminary Study
Institute of AI Education, East China Normal University（华东师范大学人工智能教育研究院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Psychology and Cognitive Science, East China Normal University（华东师范大学心理与认知科学学院）；Shanghai Changning Mental Health Center（上海市长宁区精神卫生中心）
Yu Ji, Wen Wu, Hong Zheng, Yi Hu, Xi Chen, Liang He
In recent years, personality has been regarded as a valuable personal factor being incorporated into numerous tasks such as sentiment analysis and product recommendation. This has led to widespread attention to text-based personality recognition task, which aims to identify an individual's personality based on given text. Considering that ChatGPT has recently exhibited remarkable abilities on various natural language processing tasks, we provide a preliminary evaluation of ChatGPT on text-based personality recognition task for generating effective personality data. Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level. The experimental results on two representative real-world datasets reveal that ChatGPT with zero-shot chain-of-thought prompting exhibits impressive personality recognition ability and is capable to provide natural language explanations through text-based logical reasoning. Furthermore, by employing the level-oriented prompting strategy to optimize zero-shot chain-of-thought prompting, the performance gap between ChatGPT and corresponding state-of-the-art model has been narrowed even more. However, we observe that ChatGPT shows unfairness towards certain sensitive demographic attributes such as gender and age. Additionally, we discover that eliciting the personality recognition ability of ChatGPT helps improve its performance on personality-related downstream tasks such as sentiment classification and stress prediction.
ELLIS Alicante（阿利坎特ELLIS）；University of South Bohemia（南波希米亚大学）；Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague（布拉格捷克理工大学捷克信息学、机器人与控制论研究所）
Automatic Item Generation for Personality Situational Judgment Tests with Large Language Models
Beijing Normal University（北京师范大学）；Central China Normal University（华中师范大学）；Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education（教育部青少年网络心理与行为重点实验室）；Beijing Key Laboratory of Applied Experimental Psychology（北京市应用实验心理学重点实验室）；Research Center for Capacity Building in Educational Assessment and Evaluation (Beijing Higher Education Innovation Center for Philosophy and Social Sciences)（教育评价与评估能力建设研究中心（北京高等教育哲学社会科学创新中心））
University of Augsburg（奥格斯堡大学）；SyncPilot GmbH（SyncPilot有限公司）；Nanyang Technological University（南洋理工大学）；Imperial College London（帝国理工学院）
ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks. In this work, we evaluate the capabilities of ChatGPT to perform text classification on three affective computing problems, namely, big-five personality prediction, sentiment analysis, and suicide tendency detection. We utilise three baselines, a robust language model (RoBERTa-base), a legacy word model with pretrained embeddings (Word2Vec), and a simple bag-of-words baseline (BoW). Results show that the RoBERTa tr…
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））
The Cultural Psychology of Large Language Models: Is ChatGPT a Holistic or Analytic Thinker?
New York University（纽约大学）；University of Chicago（芝加哥大学）；Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of California Los Angeles（加州大学洛杉矶分校）
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
The Chinese University of Hong Kong（香港中文大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；National University of Singapore（新加坡国立大学）；Peng Cheng Laboratory（鹏城实验室）
The Hong Kong University of Science and Technology（香港科技大学）；Human Language Technology Center, Department of Electronic and Computer Engineering（电子与计算机工程系人类语言技术中心）
We propose an end-to-end affect recognition approach using a Convolutional Neural Network (CNN) that handles multiple languages, with applications to emotion and personality recognition from speech. We lay the foundation of a universal model that is trained on multiple languages at once. As affect is shared across all languages, we are able to leverage shared information between languages and improve the overall performance for each one. We obtained an average improvement of 12.8% on emotion and 10.1% on personality when compared with the same model trained on each language only. It is end-to-…
