Language models, such as GPT-3.5 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks. However, when probing language models using a range of basic table-understanding tasks, we observe that today's language models are still sub-optimal in many table-related tasks, likely because they are pre-trained predominantly on \emph{one-dimensional} natural-language texts, whereas relational tables are \emph{two-dimensional} objects.
In this work, we propose a new "\emph{table-tuning}" paradigm, where we continue to train/fine-tune language models like GPT-3.5 and ChatGPT, using diverse table-tasks synthesized from real tables as training data, with the goal of enhancing language models' ability to understand tables and perform table tasks. We show that our resulting Table-GPT models demonstrate (1) better \emph{table-understanding} capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide-range of table tasks, including holdout unseen tasks, and (2) strong \emph{generalizability}, in its ability to respond to diverse human instructions to perform new table-tasks, in a manner similar to GPT-3.5 and ChatGPT.
School of Computer Science（计算机学院）；Fudan University（复旦大学）；Academy for Engineering Technology（工程技术研究院）；Shanghai Children’s Hospital of Fudan University（复旦大学附属儿科医院）；National Children’s Medical Center（国家儿童医学中心）
An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；School of Computer Science, Beijing Institute of Technology（北京理工大学计算机学院）；Baidu Inc.（百度公司）
本研究发现，尽管微调的 Judge Model 在域内测试集上表现优异甚至超越 GPT-4，但在泛化性、公平性和适应性上逊色于 GPT-4，其本质是任务特定分类器，无法作为通用替代品。
Exploring Combinatorial Problem Solving with Large Language Models: A Case Study on the Travelling Salesman Problem Using GPT-3.5 Turbo
King Fahd University of Petroleum & Minerals（法赫德国王石油矿产大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-法赫德国王石油矿产大学人工智能联合研究中心）；Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals（法赫德国王石油矿产大学智能移动与物流中心）；Queensland University of Technology（昆士兰科技大学）
Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA
Seoul National University（首尔大学）；Ewha Womans University Medical Center（梨花女子大学医疗院）；Seoul National University Hospital and College of Medicine（首尔大学医院暨医学院）
MedSlice: Fine-Tuned Large Language Models for Secure Clinical Note Sectioning
Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Albany Medical College（奥尔巴尼医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；McGill University（麦吉尔大学）；Harvard Medical School（哈佛医学院）
Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization
New York University（纽约大学）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；University of California San Diego（加州大学圣地亚哥分校）
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
The University of Georgia（佐治亚大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Northwestern Polytechnical University（西北工业大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Mayo Clinic（梅奥诊所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；Texas A&M University（得克萨斯农工大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；University of Washington（华盛顿大学）
Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物医学信息学院）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；Mayo Clinic（梅奥诊所）；School of Medicine, Yale University（耶鲁大学医学院）
A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing
Department of Computing；Games, Teesside University, Middlesbrough, United Kingdom Centre for Digital Innovation, Teesside University, Middlesbrough, United Kingdom Faculty of Business \& Technology, University of Sunderland, Sunderland, United Kingdom
