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
Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study
Daniel Smolyak, Arshana Welivita, Margrét V. Bjarnadóttir, Ritu Agarwal
Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech
Jonathan Heitz, Gerold Schneider, Nicolas Langer
OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
Akari Asai, Jacqueline He, Rulin Shao, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D'arcy, David Wadden, Matt Latzke, Minyang Tian, Pan Ji, Shengyan Liu, Hao Tong, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Graham Neubig, Dan Weld, Doug Downey, Wen-tau Yih, Pang Wei Koh, Hannaneh Hajishirzi
Explaining GPT-4's Schema of Depression Using Machine Behavior Analysis
Adithya V Ganesan, Vasudha Varadarajan, Yash Kumar Lal, Veerle C. Eijsbroek, Katarina Kjell, Oscar N.E. Kjell, Tanuja Dhanasekaran, Elizabeth C. Stade, Johannes C. Eichstaedt, Ryan L. Boyd, H. Andrew Schwartz, Lucie Flek
Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels
Jianhao Yan, Pingchuan Yan, Yulong Chen, Jing Li, Xianchao Zhu, Yue Zhang