{"total":68,"rows":[{"id":"2203.10244","title":"ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning","created":"2022-03-19","cited_by_count":1479,"area":"lr"},{"id":"2108.11896","title":"A Survey on Automated Fact-Checking","created":"2021-08-26","cited_by_count":378,"area":null},{"id":"2210.03347","title":"Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding","created":"2022-10-07","cited_by_count":366,"area":null},{"id":"2305.09645","title":"StructGPT: A General Framework for Large Language Model to Reason over Structured Data","created":"2023-05-16","cited_by_count":343,"area":"lr"},{"id":"2107.07653","title":"TAPEX: Table Pre-training via Learning a Neural SQL Executor","created":"2021-07-16","cited_by_count":235,"area":null},{"id":"2210.02875","title":"Binding Language Models in Symbolic Languages","created":"2022-10-06","cited_by_count":228,"area":null},{"id":"2401.04398","title":"Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding","created":"2024-01-09","cited_by_count":188,"area":"lr"},{"id":"2104.00369","title":"FeTaQA: Free-form Table Question Answering","created":"2021-04-01","cited_by_count":175,"area":null},{"id":"2212.09662","title":"MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering","created":"2022-12-19","cited_by_count":133,"area":"lr"},{"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":"2210.06710","title":"Large Language Models are few(1)-shot Table Reasoners","created":"2022-10-13","cited_by_count":116,"area":"lr"},{"id":"2203.00274","title":"TableFormer: Robust Transformer Modeling for Table-Text Encoding","created":"2022-03-01","cited_by_count":91,"area":null},{"id":"2103.12011","title":"Open Domain Question Answering over Tables via Dense Retrieval","created":"2021-03-22","cited_by_count":74,"area":null},{"id":"2305.09696","title":"Generative Table Pre-training Empowers Models for Tabular Prediction","created":"2023-05-16","cited_by_count":55,"area":null},{"id":"2109.04312","title":"MATE: Multi-view Attention for Table Transformer Efficiency","created":"2021-09-09","cited_by_count":54,"area":null},{"id":"2403.12027","title":"From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models","created":"2024-03-18","cited_by_count":53,"area":null},{"id":"2104.04725","title":"Fool Me Twice: Entailment from Wikipedia Gamification","created":"2021-04-10","cited_by_count":50,"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":"2402.08327","title":"PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers","created":"2024-02-13","cited_by_count":42,"area":"mllm"},{"id":"2211.02816","title":"PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training","created":"2022-11-05","cited_by_count":41,"area":null},{"id":"2410.04739","title":"TableRAG: Million-Token Table Understanding with Language Models","created":"2024-10-07","cited_by_count":41,"area":null},{"id":"2107.07261","title":"Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills","created":"2021-07-15","cited_by_count":39,"area":null},{"id":"2210.12374","title":"ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples","created":"2022-10-22","cited_by_count":29,"area":"lr"},{"id":"2311.08002","title":"TempTabQA: Temporal Question Answering for Semi-Structured Tables","created":"2023-11-14","cited_by_count":27,"area":null},{"id":"2104.04243","title":"Incorporating External Knowledge to Enhance Tabular Reasoning","created":"2021-04-09","cited_by_count":23,"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":"2411.08516","title":"Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding","created":"2024-11-13","cited_by_count":20,"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":"2210.05197","title":"Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA","created":"2022-10-11","cited_by_count":17,"area":null},{"id":"2205.01538","title":"SUBS: Subtree Substitution for Compositional Semantic Parsing","created":"2022-05-03","cited_by_count":16,"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":"2104.01099","title":"TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training","created":"2021-04-02","cited_by_count":14,"area":null},{"id":"2304.07995","title":"From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning","created":"2023-04-17","cited_by_count":14,"area":null},{"id":"2204.08753","title":"Table-based Fact Verification with Self-adaptive Mixture of Experts","created":"2022-04-19","cited_by_count":12,"area":"moe"},{"id":"2108.04049","title":"Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models","created":"2021-08-09","cited_by_count":11,"area":null},{"id":"2109.11020","title":"Exploring Decomposition for Table-based Fact Verification","created":"2021-09-22","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":"2310.00789","title":"Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks","created":"2023-10-01","cited_by_count":10,"area":null},{"id":"2108.00578","title":"Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning","created":"2021-08-02","cited_by_count":9,"area":null},{"id":"2109.04053","title":"Table-based Fact Verification with Salience-aware Learning","created":"2021-09-09","cited_by_count":9,"area":null},{"id":"2409.16682","title":"SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA","created":"2024-09-25","cited_by_count":9,"area":null},{"id":"2106.00479","title":"DoT: An efficient Double Transformer for NLP tasks with tables","created":"2021-06-01","cited_by_count":8,"area":null},{"id":"2407.16030","title":"Enhancing Temporal Understanding in LLMs for Semi-structured Tables","created":"2024-07-22","cited_by_count":8,"area":"lr"},{"id":"2212.10097","title":"Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework","created":"2022-12-20","cited_by_count":6,"area":null},{"id":"2305.04782","title":"HistAlign: Improving Context Dependency in Language Generation by Aligning with History","created":"2023-05-08","cited_by_count":6,"area":null},{"id":"2311.08662","title":"Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets","created":"2023-11-15","cited_by_count":6,"area":null},{"id":"2405.19094","title":"Faithful Chart Summarization with ChaTS-Pi","created":"2024-05-29","cited_by_count":5,"area":null},{"id":"2503.11921","title":"RePanda: Pandas-powered Tabular Verification and Reasoning","created":"2025-03-14","cited_by_count":5,"area":null},{"id":"2109.06480","title":"Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification","created":"2021-09-14","cited_by_count":4,"area":null},{"id":"2210.12795","title":"Realistic Data Augmentation Framework for Enhancing Tabular Reasoning","created":"2022-10-23","cited_by_count":4,"area":null},{"id":"2106.00248","title":"Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning","created":"2021-06-01","cited_by_count":3,"area":null},{"id":"2210.12259","title":"Enhancing Tabular Reasoning with Pattern Exploiting Training","created":"2022-10-21","cited_by_count":3,"area":"lr"},{"id":"2211.12641","title":"Leveraging Data Recasting to Enhance Tabular Reasoning","created":"2022-11-23","cited_by_count":3,"area":null},{"id":"2307.03313","title":"InfoSync: Information Synchronization across Multilingual Semi-structured Tables","created":"2023-07-06","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":"2508.17005","title":"Planning for Success: Exploring LLM Long-term Planning Capabilities in Table Understanding","created":"2025-08-23","cited_by_count":2,"area":null},{"id":"2509.15356","title":"Predicting Language Models' Success at Zero-Shot Probabilistic Prediction","created":"2025-09-18","cited_by_count":2,"area":null},{"id":"2104.03071","title":"BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with Tables","created":"2021-04-07","cited_by_count":1,"area":null},{"id":"2402.13288","title":"Training Table Question Answering via SQL Query Decomposition","created":"2024-02-19","cited_by_count":1,"area":null},{"id":"2407.01393","title":"POLygraph: Polish Fake News Dataset","created":"2024-07-01","cited_by_count":1,"area":null},{"id":"2508.06110","title":"PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion","created":"2025-08-08","cited_by_count":1,"area":"lr"},{"id":"2510.27238","title":"DRAMA: Unifying Data Retrieval and Analysis for Open-Domain Analytic Queries","created":"2025-10-31","cited_by_count":1,"area":null},{"id":"2602.17937","title":"Analyzing LLM Instruction Optimization for Tabular Fact Verification","created":"2026-02-20","cited_by_count":1,"area":"lr"},{"id":"2306.11696","title":"RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training","created":"2023-06-20","cited_by_count":0,"area":null},{"id":"2307.16208","title":"Around the GLOBE: Numerical Aggregation Question-Answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks","created":"2023-07-30","cited_by_count":0,"area":null},{"id":"2511.13225","title":"Seeing isn't Hearing: Benchmarking Vision Language Models at Interpreting Spectrograms","created":"2025-11-17","cited_by_count":0,"area":"mllm"},{"id":"2605.00199","title":"RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners","created":"2026-04-30","cited_by_count":0,"area":"lr"}],"next_cursor":null}