ChatGPT Generated Conversation
Research on ChatGPT-generated conversations explores how this large language model (LLM) performs in various interactive contexts, focusing on its capabilities, limitations, and potential biases. Current studies investigate its application in diverse fields, including education (e.g., essay scoring, literature review assistance), healthcare (e.g., medication management, robot interaction), and software development (e.g., code generation, library recommendation), often comparing its performance to human experts or other AI models. This work is significant because it helps assess the reliability and ethical implications of LLMs in real-world applications, informing the development of responsible AI and highlighting potential risks and benefits across numerous sectors.
Papers
An Empirical Study on Information Extraction using Large Language Models
Ridong Han, Chaohao Yang, Tao Peng, Prayag Tiwari, Xiang Wan, Lu Liu, Benyou Wang
Embrace Opportunities and Face Challenges: Using ChatGPT in Undergraduate Students' Collaborative Interdisciplinary Learning
Gaoxia Zhu, Xiuyi Fan, Chenyu Hou, Tianlong Zhong, Peter Seow, Annabel Chen Shen-Hsing, Preman Rajalingam, Low Kin Yew, Tan Lay Poh
Does ChatGPT have Theory of Mind?
Bart Holterman, Kees van Deemter
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, Kailong Wang, Yang Liu
ChatGPT as your Personal Data Scientist
Md Mahadi Hassan, Alex Knipper, Shubhra Kanti Karmaker Santu
LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation
Siyuan Chen, Mengyue Wu, Kenny Q. Zhu, Kunyao Lan, Zhiling Zhang, Lyuchun Cui
Fairness of ChatGPT
Yunqi Li, Lanjing Zhang, Yongfeng Zhang
Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students
Katherine Abramski, Salvatore Citraro, Luigi Lombardi, Giulio Rossetti, Massimo Stella
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
Jan Cegin, Jakub Simko, Peter Brusilovsky
Automatic Code Summarization via ChatGPT: How Far Are We?
Weisong Sun, Chunrong Fang, Yudu You, Yun Miao, Yi Liu, Yuekang Li, Gelei Deng, Shenghan Huang, Yuchen Chen, Quanjun Zhang, Hanwei Qian, Yang Liu, Zhenyu Chen
Comparing Software Developers with ChatGPT: An Empirical Investigation
Nathalia Nascimento, Paulo Alencar, Donald Cowan
ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text Ambiguation to Expand Mental Health Care Delivery
Anaelia Ovalle, Mehrab Beikzadeh, Parshan Teimouri, Kai-Wei Chang, Majid Sarrafzadeh