{"total":43,"rows":[{"id":"2402.17944","title":"Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey","created":"2024-02-27","cited_by_count":159,"area":null},{"id":"2404.09491","title":"Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning","created":"2024-04-15","cited_by_count":81,"area":null},{"id":"2306.07951","title":"Questioning the Survey Responses of Large Language Models","created":"2023-06-13","cited_by_count":74,"area":null},{"id":"2403.01841","title":"Making Pre-trained Language Models Great on Tabular Prediction","created":"2024-03-04","cited_by_count":57,"area":null},{"id":"2502.17361","title":"A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities","created":"2025-02-24","cited_by_count":41,"area":null},{"id":"2504.16109","title":"Representation Learning for Tabular Data: A Comprehensive Survey","created":"2025-04-17","cited_by_count":36,"area":null},{"id":"2310.07338","title":"From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models","created":"2023-10-11","cited_by_count":24,"area":null},{"id":"2307.04308","title":"Towards Cross-Table Masked Pretraining for Web Data Mining","created":"2023-07-10","cited_by_count":21,"area":null},{"id":"2408.10548","title":"Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution","created":"2024-08-20","cited_by_count":19,"area":null},{"id":"2505.11545","title":"TARGET: Benchmarking Table Retrieval for Generative Tasks","created":"2025-05-14","cited_by_count":17,"area":null},{"id":"2402.01204","title":"A Survey on Self-Supervised Learning for Non-Sequential Tabular Data","created":"2024-02-02","cited_by_count":15,"area":null},{"id":"2312.05114","title":"The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against \"Truly Anonymous\" Synthetic Datasets","created":"2023-12-08","cited_by_count":14,"area":null},{"id":"2310.07736","title":"Observatory: Characterizing Embeddings of Relational Tables","created":"2023-10-05","cited_by_count":12,"area":null},{"id":"2403.20208","title":"Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining","created":"2024-03-29","cited_by_count":12,"area":null},{"id":"2502.11596","title":"LLM Embeddings for Deep Learning on Tabular Data","created":"2025-02-17","cited_by_count":12,"area":null},{"id":"2505.22450","title":"Position: All Current Generative Fidelity and Diversity Metrics are Flawed","created":"2025-05-28","cited_by_count":12,"area":null},{"id":"2406.14541","title":"Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)","created":"2024-06-20","cited_by_count":10,"area":null},{"id":"2502.16414","title":"TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation","created":"2025-02-23","cited_by_count":10,"area":null},{"id":"2410.21717","title":"Generating Realistic Tabular Data with Large Language Models","created":"2024-10-29","cited_by_count":9,"area":null},{"id":"2412.14056","title":"A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future","created":"2024-12-18","cited_by_count":9,"area":null},{"id":"2507.18504","title":"Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models","created":"2025-07-24","cited_by_count":8,"area":null},{"id":"2410.07851","title":"Scalable Representation Learning for Multimodal Tabular Transactions","created":"2024-10-10","cited_by_count":5,"area":null},{"id":"2309.17339","title":"Scaling Experiments in Self-Supervised Cross-Table Representation Learning","created":"2023-09-29","cited_by_count":4,"area":null},{"id":"2311.17453","title":"Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions","created":"2023-11-29","cited_by_count":4,"area":null},{"id":"2406.11391","title":"P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models","created":"2024-06-17","cited_by_count":4,"area":null},{"id":"2501.16247","title":"Zero-Shot Decision Tree Construction via Large Language Models","created":"2025-01-27","cited_by_count":4,"area":null},{"id":"2510.09783","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2404.12580","title":"iTBLS: A Dataset of Interactive Conversations Over Tabular Information","created":"2024-04-19","cited_by_count":3,"area":null},{"id":"2406.10922","title":"Generating Tables from the Parametric Knowledge of Language Models","created":"2024-06-16","cited_by_count":3,"area":null},{"id":"2407.09790","title":"Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs","created":"2024-07-13","cited_by_count":3,"area":null},{"id":"2503.02645","title":"A Generalized Theory of Mixup for Structure-Preserving Synthetic Data","created":"2025-03-03","cited_by_count":2,"area":null},{"id":"2503.09850","title":"TabNSA: Native Sparse Attention for Efficient Tabular Data Learning","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2605.04962","title":"TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding","created":"2026-05-06","cited_by_count":2,"area":null},{"id":"2502.15819","title":"Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting","created":"2025-02-20","cited_by_count":1,"area":null},{"id":"2502.20268","title":"Large Language Models as Attribution Regularizers for Efficient Model Training","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2507.23227","title":"Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs","created":"2025-07-31","cited_by_count":1,"area":null},{"id":"2510.03904","title":"LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis","created":"2025-10-04","cited_by_count":1,"area":null},{"id":"2312.17072","title":"An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation","created":"2023-12-28","cited_by_count":0,"area":null},{"id":"2409.06624","title":"A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio","created":"2024-09-10","cited_by_count":0,"area":null},{"id":"2508.02601","title":"StructSynth: Dependency Graphs as Generation Plans for Low-Data Tabular Synthesis with Language Models","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2603.10937","title":"Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2603.17191","title":"Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data","created":"2026-03-17","cited_by_count":0,"area":null},{"id":"2607.23286","title":"Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs","created":"2026-07-25","cited_by_count":0,"area":null}],"next_cursor":null}