{"total":60,"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":"2206.01347","title":"MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data","created":"2022-06-03","cited_by_count":121,"area":null},{"id":"2406.14991","title":"SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation","created":"2024-06-21","cited_by_count":111,"area":null},{"id":"2401.12869","title":"TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks","created":"2024-01-23","cited_by_count":75,"area":null},{"id":"2407.09413","title":"SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers","created":"2024-07-12","cited_by_count":75,"area":null},{"id":"2403.15452","title":"What Are Tools Anyway? A Survey from the Language Model Perspective","created":"2024-03-18","cited_by_count":54,"area":null},{"id":"2306.14321","title":"RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations","created":"2023-06-25","cited_by_count":43,"area":null},{"id":"2201.09745","title":"Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks","created":"2022-01-24","cited_by_count":42,"area":null},{"id":"2207.05270","title":"A Survey on Table Question Answering: Recent Advances","created":"2022-07-12","cited_by_count":41,"area":null},{"id":"2205.03972","title":"Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning","created":"2022-05-08","cited_by_count":32,"area":null},{"id":"2305.12820","title":"MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering","created":"2023-05-22","cited_by_count":32,"area":null},{"id":"2503.18943","title":"SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding","created":"2025-03-24","cited_by_count":30,"area":null},{"id":"2402.12869","title":"Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data","created":"2024-02-20","cited_by_count":24,"area":null},{"id":"2402.01155","title":"CABINET: Content Relevance based Noise Reduction for Table Question Answering","created":"2024-02-02","cited_by_count":23,"area":null},{"id":"2109.07323","title":"FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining","created":"2021-09-15","cited_by_count":19,"area":null},{"id":"2501.19378","title":"TableMaster: A Recipe to Advance Table Understanding with Language Models","created":"2025-01-31","cited_by_count":18,"area":null},{"id":"2407.09025","title":"SpreadsheetLLM: Encoding Spreadsheets for Large Language Models","created":"2024-07-12","cited_by_count":17,"area":null},{"id":"2411.19504","title":"TQA-Bench: Evaluating LLMs for Multi-Table Question Answering","created":"2024-11-29","cited_by_count":17,"area":null},{"id":"2205.12697","title":"PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2310.14687","title":"API-Assisted Code Generation for Question Answering on Varied Table Structures","created":"2023-10-23","cited_by_count":14,"area":null},{"id":"2305.14987","title":"Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios","created":"2023-05-24","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":"2506.13405","title":"RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis","created":"2025-06-16","cited_by_count":12,"area":null},{"id":"2204.03357","title":"Parameter-Efficient Abstractive Question Answering over Tables or Text","created":"2022-04-07","cited_by_count":11,"area":null},{"id":"2406.08398","title":"cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers","created":"2024-06-12","cited_by_count":11,"area":null},{"id":"2305.03458","title":"Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question","created":"2023-05-05","cited_by_count":10,"area":null},{"id":"2504.06560","title":"NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables","created":"2025-04-09","cited_by_count":8,"area":null},{"id":"2302.14169","title":"TabGenie: A Toolkit for Table-to-Text Generation","created":"2023-02-27","cited_by_count":7,"area":null},{"id":"2512.00947","title":"Table as a Modality for Large Language Models","created":"2025-11-30","cited_by_count":7,"area":null},{"id":"2505.23174","title":"Map&Make: Schema Guided Text to Table Generation","created":"2025-05-29","cited_by_count":6,"area":null},{"id":"2405.08099","title":"KET-QA: A Dataset for Knowledge Enhanced Table Question Answering","created":"2024-05-13","cited_by_count":5,"area":null},{"id":"2505.14131","title":"Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering","created":"2025-05-20","cited_by_count":5,"area":null},{"id":"2509.21205","title":"TABLET: A Large-Scale Dataset for Robust Visual Table Understanding","created":"2025-09-25","cited_by_count":5,"area":null},{"id":"2510.09671","title":"Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation","created":"2025-10-08","cited_by_count":5,"area":null},{"id":"2606.25178","title":"Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR","created":"2026-06-23","cited_by_count":5,"area":null},{"id":"2211.03462","title":"NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering","created":"2022-11-07","cited_by_count":4,"area":null},{"id":"2503.04396","title":"TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models","created":"2025-03-06","cited_by_count":4,"area":null},{"id":"2511.13283","title":"TabFlash: Efficient Table Understanding with Progressive Question Conditioning and Token Focusing","created":"2025-11-17","cited_by_count":4,"area":null},{"id":"2212.08780","title":"Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations","created":"2022-12-17","cited_by_count":3,"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":"2409.19700","title":"2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models","created":"2024-09-29","cited_by_count":3,"area":null},{"id":"2509.14671","title":"TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding","created":"2025-09-18","cited_by_count":3,"area":null},{"id":"2511.04584","title":"Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis","created":"2025-11-06","cited_by_count":3,"area":null},{"id":"2501.14717","title":"What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects","created":"2025-01-24","cited_by_count":2,"area":null},{"id":"2507.22069","title":"A Compute-Matched Re-Evaluation of TroVE on MATH","created":"2025-07-16","cited_by_count":2,"area":null},{"id":"2504.20047","title":"HCT-QA: A Benchmark for Question Answering on Human-Centric Tables","created":"2025-03-09","cited_by_count":1,"area":null},{"id":"2505.17565","title":"p2-TQA: A Process-based Preference Learning Framework for Self-Improving Table Question Answering Models","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2605.01018","title":"WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild","created":"2026-05-01","cited_by_count":1,"area":null},{"id":"2605.05955","title":"TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2305.07475","title":"Comprehensive Solution Program Centric Pretraining for Table-and-Text Hybrid Numerical Reasoning","created":"2023-05-12","cited_by_count":0,"area":null},{"id":"2310.15464","title":"Interpreting Answers to Yes-No Questions in User-Generated Content","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2510.18173","title":"Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2511.23220","title":"Instruction Tuning of Large Language Models for Tabular Data Generation-in One Day","created":"2025-11-28","cited_by_count":0,"area":null},{"id":"2512.15907","title":"TabReX : Tabular Referenceless eXplainable Evaluation","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2602.20017","title":"QUIETT: Query-Independent Table Transformation for Robust Reasoning","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2603.08910","title":"SciTaRC: A Plan-Annotated Scientific Tabular QA Benchmark for Language Reasoning and Complex Computation","created":"2026-03-09","cited_by_count":0,"area":null},{"id":"2603.16245","title":"How to Utilize Complementary Vision-Text Information for 2D Structure Understanding","created":"2026-03-17","cited_by_count":0,"area":null},{"id":"2604.16099","title":"DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2606.28916","title":"Latent Bridges for Multi-Table Question Answering","created":"2026-06-27","cited_by_count":0,"area":null},{"id":"2609.07707","title":"DeepTable: Structural Attention Biases and Tree Path Encoding for Hierarchical Table Understanding","created":"2026-09-07","cited_by_count":0,"area":null}],"next_cursor":null}