Research on differentially private synthetic tabular data has largely focused on independent and identically distributed rows where each record corresponds to a unique individual. This perspective neglects the temporal complexity in longitudinal datasets, such as electronic health records, where a user contributes an entire (sub) table of sequential events. While practitioners might attempt to model such data by flattening user histories into high-dimensional vectors for use with standard marginal-based mechanisms, we demonstrate that this strategy is insufficient. Flattening fails to preserve temporal coherence even when it maintains valid marginal distributions. We introduce PATH, a novel generative framework that treats the full table as the unit of synthesis and leverages the autoregressive capabilities of privately fine-tuned large language models. Extensive evaluations show that PATH effectively captures long-range dependencies that traditional methods miss. Empirically, our method reduces the distributional distance to real trajectories by over 60% and reduces state transition errors by nearly 50% compared to leading marginal mechanisms while achieving similar marginal fidelity.
TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models
Shanghai University of Finance and Economics（上海财经大学）；Peking University（北京大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft Research（微软研究院）
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
University of Pennsylvania（宾夕法尼亚大学）；Stony Brook University（石溪大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Medical University of South Carolina（南卡罗来纳医科大学）；Cedars-Sinai Medical Center（西格拉姆山医疗中心）
ERASMO: Leveraging Large Language Models for Enhanced Clustering Segmentation
Hub de Inteligência Artificial e Arquiteturas Cognitivas (H.IAAC)（人工智能与认知架构中心（H.IAAC））；Artificial Intelligence Laboratory (Recod.ai)（人工智能实验室（Recod.ai））；Instituto de Computação, Universidade Estadual de Campinas (UNICAMP)（坎皮纳斯州立大学计算机研究所（UNICAMP））；Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas (UNICAMP)（坎皮纳斯州立大学机械工程学院（UNICAMP））
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学胃肠病与肝病研究所）；Faculty of Medicine, Isfahan University of Medical Sciences（伊斯法罕医科大学医学院）；Faculty of Business and Information Technology, Ontario Tech University（安大略理工大学商业与信息技术学院）；School of Medicine, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学医学院）；Digestive Disease Research Institute, Tehran University of Medical Sciences（德黑兰医科大学消化疾病研究所）；Department of Surgery, The Johns Hopkins University（约翰霍普金斯大学外科学系）；Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学护理与助产学院学生研究委员会）；MPH department, Shiraz University of Medical Sciences（设拉子医科大学公共卫生硕士系）；Department of Emergency Medicine, School of Medicine, Safety Promotion and Injury Prevention Research Center, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学伊玛目侯赛因医院医学院急诊医学系安全促进与伤害预防研究中心）；Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center（西达赛奈医疗中心卡什胃肠病与肝病科）；Department of Medicine, Stanford University（斯坦福大学医学系）；Division of Data Driven and Digital Health (D3M), The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院查尔斯·布朗夫曼个性化医学研究所数据驱动与数字健康部）；Infectious Diseases and Tropical Medicine Research Center, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学传染病与热带医学研究中心）；Department of Infectious Diseases, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学洛格曼·哈基姆医院传染病科）；National Institute for Health and Care Research (NIHR), Nottingham Biomedical Research Centre, Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham（诺丁汉大学医学院国家健康与护理研究所诺丁汉生物医学研究中心听力科学、心理健康与临床神经科学）
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
University of Colorado（科罗拉多大学）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Mass General Brigham, Harvard Medical School（哈佛医学院麻省总医院）；Boston Children Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Loyola University Chicago（芝加哥洛约拉大学）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Michigan（密歇根大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Far Eastern Memorial Hospital（远东纪念医院）
Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
Huazhong University of Science and Technology（华中科技大学）；Chongqing University（重庆大学）；University of New South Wales（新南威尔士大学）；University of Technology Sydney（悉尼科技大学）
When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
Institute for Computational and Mathematical Engineering（计算与数学工程研究所）；Stanford University（斯坦福大学）；Graduate School of Business（商学院）；Harvard Data Science Initiative（哈佛大学数据科学倡议）；Harvard University（哈佛大学）
