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
3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V
Dingning Liu, Xiaomeng Dong, Renrui Zhang, Xu Luo, Peng Gao, Xiaoshui Huang, Yongshun Gong, Zhihui Wang
Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models
Xin Jin, Jonathan Larson, Weiwei Yang, Zhiqiang Lin
GPT-4 Surpassing Human Performance in Linguistic Pragmatics
Ljubisa Bojic, Predrag Kovacevic, Milan Cabarkapa
GPT-4 and Safety Case Generation: An Exploratory Analysis
Mithila Sivakumar, Alvine Boaye Belle, Jinjun Shan, Kimya Khakzad Shahandashti
Image and Data Mining in Reticular Chemistry Using GPT-4V
Zhiling Zheng, Zhiguo He, Omar Khattab, Nakul Rampal, Matei A. Zaharia, Christian Borgs, Jennifer T. Chayes, Omar M. Yaghi
A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education
Jacob Doughty, Zipiao Wan, Anishka Bompelli, Jubahed Qayum, Taozhi Wang, Juran Zhang, Yujia Zheng, Aidan Doyle, Pragnya Sridhar, Arav Agarwal, Christopher Bogart, Eric Keylor, Can Kultur, Jaromir Savelka, Majd Sakr
GPT4Point: A Unified Framework for Point-Language Understanding and Generation
Zhangyang Qi, Ye Fang, Zeyi Sun, Xiaoyang Wu, Tong Wu, Jiaqi Wang, Dahua Lin, Hengshuang Zhao