GPT 4
GPT-4, a large language model, is being extensively researched for its capabilities across diverse tasks, including translation, code analysis, educational assessment, and medical information extraction. Current research focuses on evaluating its performance against human benchmarks, exploring its limitations (e.g., susceptibility to prompt engineering and inconsistencies in complex reasoning), and developing methods to improve its reliability and efficiency, including the use of prompt engineering and ensemble methods with other machine learning models. These investigations are crucial for understanding GPT-4's strengths and weaknesses, informing its responsible deployment in various applications, and advancing the broader field of large language model development.
Papers
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
Yikun Sun, Zhen Wan, Nobuhiro Ueda, Sakiko Yahata, Fei Cheng, Chenhui Chu, Sadao Kurohashi
Assessing the Aesthetic Evaluation Capabilities of GPT-4 with Vision: Insights from Group and Individual Assessments
Yoshia Abe, Tatsuya Daikoku, Yasuo Kuniyoshi
Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4
Angus Yang, Zehan Li, Jie Li
Can Large Language Models be Used to Provide Psychological Counselling? An Analysis of GPT-4-Generated Responses Using Role-play Dialogues
Michimasa Inaba, Mariko Ukiyo, Keiko Takamizo
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4
Ting Fang Tan, Kabilan Elangovan, Liyuan Jin, Yao Jie, Li Yong, Joshua Lim, Stanley Poh, Wei Yan Ng, Daniel Lim, Yuhe Ke, Nan Liu, Daniel Shu Wei Ting
GPT-4's assessment of its performance in a USMLE-based case study
Uttam Dhakal, Aniket Kumar Singh, Suman Devkota, Yogesh Sapkota, Bishal Lamichhane, Suprinsa Paudyal, Chandra Dhakal