In the era of data-driven decision-making, accurate table-level representations and efficient table recommendation systems are becoming increasingly crucial for improving table management, discovery, and analysis. However, existing approaches to tabular data representation often face limitations, primarily due to their focus on cell-level tasks and the lack of high-quality training data. To address these challenges, we first formulate a clear definition of table similarity in the context of data transformation activities within data-driven enterprises. This definition serves as the foundation for synthetic data generation, which require a well-defined data generation process. Building on this, we propose a novel synthetic data generation pipeline that harnesses the code generation and data manipulation capabilities of Large Language Models (LLMs) to create a large-scale synthetic dataset tailored for table-level representation learning. Through manual validation and performance comparisons on the table recommendation task, we demonstrate that the synthetic data generated by our pipeline aligns with our proposed definition of table similarity and significantly enhances table representations, leading to improved recommendation performance.
Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎-罗克Quantin 国家信息与自动化研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
Enhancing Table Reasoning with Deterministic Table-State Rewards
University of California, Los Angeles（加州大学洛杉矶分校）；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；University College London（伦敦大学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Manitoba（曼尼托巴大学）
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；Artificial Intelligence Research Institute, iFLYTEK Co., Ltd（iFLYTEK公司人工智能研究院）
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室，计算机科学与工程学院，北京航空航天大学，北京，中国）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京，中国）；Center for AI Business Innovation, Department of Management Science and Systems, School of Management, University at Buffalo, USA（人工智能商业创新中心，管理科学与系统系，管理学院，布法罗大学，美国）
Enhancing Hepatopathy Clinical Trial Efficiency: A Secure, Large Language Model-Powered Pre-Screening Pipeline
The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine（广西中医药大学第一附属医院）；Institute of Biointellgence Technology, BGI Research（华大研究院生物智能技术研究所）；Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research（华大研究院广东省生命科学大数据工程技术研究中心）
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
Florida State University（佛罗里达州立大学）；University of Washington（华盛顿大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
Population Health Sciences, Weill Cornell Medicine（人口健康科学系，韦尔·科恩医学中心）；Donald Bren School of Information and Computer Sciences, University of California, Irvine（唐纳德·布伦信息与计算机科学学院，加州大学伊尔弗分校）
TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering
University of California, Los Angeles（加州大学洛杉矶分校）；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；SimpleWay
MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电气与计算机工程系）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）
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）
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection
School of Cyber Science Engineering, Huazhong University of Science and Technology（华中科技大学网络空间科学与工程学院）；Wuhan AI Research Foundation Model Research Center（武汉人工智能研究基金会大模型研究中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion
Department of Biostatistics, Columbia Mailman School of Public Health（哥伦比亚大学生物统计学系、哥伦比亚大学马利曼公共卫生学院）；Institute for Data, Systems, and Society, Massachusetts Institute of Technology（麻省理工学院数据、系统和社会研究所）；Department of Biostatistics, Yale School of Public Health（耶鲁大学生物统计学系）
