{"total":137,"rows":[{"id":"2403.12895","title":"mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding","created":"2024-03-19","cited_by_count":220,"area":null},{"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":"2402.09391","title":"LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset","created":"2024-02-14","cited_by_count":128,"area":null},{"id":"2405.12819","title":"Large Language Models Meet NLP: A Survey","created":"2024-05-21","cited_by_count":103,"area":null},{"id":"2406.10833","title":"A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery","created":"2024-06-16","cited_by_count":96,"area":null},{"id":"2403.19318","title":"TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios","created":"2024-03-28","cited_by_count":77,"area":null},{"id":"2411.02059","title":"TableGPT2: A Large Multimodal Model with Tabular Data Integration","created":"2024-11-04","cited_by_count":72,"area":"mllm"},{"id":"2402.17644","title":"Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data","created":"2024-02-27","cited_by_count":63,"area":"lr"},{"id":"2402.05121","title":"Large Language Model for Table Processing: A Survey","created":"2024-02-04","cited_by_count":52,"area":null},{"id":"2406.08100","title":"Multimodal Table Understanding","created":"2024-06-12","cited_by_count":44,"area":null},{"id":"2406.07835","title":"SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature","created":"2024-06-10","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":"2402.12424","title":"Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs","created":"2024-02-19","cited_by_count":40,"area":"lr"},{"id":"2505.12415","title":"Table-R1: Region-based Reinforcement Learning for Table Understanding","created":"2025-05-18","cited_by_count":37,"area":"lr"},{"id":"2312.16702","title":"Rethinking Tabular Data Understanding with Large Language Models","created":"2023-12-27","cited_by_count":36,"area":"lr"},{"id":"2510.23587","title":"A Survey of Data Agents: Emerging Paradigm or Overstated Hype?","created":"2025-10-27","cited_by_count":30,"area":"agents"},{"id":"2402.08259","title":"A Survey of Table Reasoning with Large Language Models","created":"2024-02-13","cited_by_count":29,"area":"lr"},{"id":"2402.16671","title":"StructLM: Towards Building Generalist Models for Structured Knowledge Grounding","created":"2024-02-26","cited_by_count":29,"area":null},{"id":"2505.23621","title":"Table-R1: Inference-Time Scaling for Table Reasoning","created":"2025-05-29","cited_by_count":24,"area":"lr"},{"id":"2403.02177","title":"ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context","created":"2024-03-04","cited_by_count":23,"area":"lr"},{"id":"2312.01678","title":"Jellyfish: A Large Language Model for Data Preprocessing","created":"2023-12-04","cited_by_count":20,"area":null},{"id":"2505.14415","title":"Table Foundation Models: on knowledge pre-training for tabular learning","created":"2025-05-20","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":"2504.01346","title":"RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking","created":"2025-04-02","cited_by_count":18,"area":"rag"},{"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":"2406.17961","title":"NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization","created":"2024-06-25","cited_by_count":16,"area":"lr"},{"id":"2510.06162","title":"TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts","created":"2025-10-07","cited_by_count":16,"area":null},{"id":"2501.13042","title":"Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning","created":"2025-01-22","cited_by_count":15,"area":"lr"},{"id":"2506.03949","title":"TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering","created":"2025-06-04","cited_by_count":15,"area":null},{"id":"2412.04272","title":"PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables","created":"2024-12-05","cited_by_count":14,"area":"lr"},{"id":"2311.09818","title":"SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models","created":"2023-11-16","cited_by_count":13,"area":null},{"id":"2412.11711","title":"MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning","created":"2024-12-16","cited_by_count":13,"area":"lr"},{"id":"2412.20145","title":"Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering","created":"2024-12-28","cited_by_count":13,"area":"agents"},{"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":"2406.10382","title":"Efficient Prompting for LLM-based Generative Internet of Things","created":"2024-06-14","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":"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":"2502.18125","title":"HyperG: Hypergraph-Enhanced LLMs for Structured Knowledge","created":"2025-02-25","cited_by_count":11,"area":null},{"id":"2508.18190","title":"ST-Raptor: LLM-Powered Semi-Structured Table Question Answering","created":"2025-08-25","cited_by_count":11,"area":null},{"id":"2312.09634","title":"Vectorizing string entries for data processing on tables: when are larger language models better?","created":"2023-12-15","cited_by_count":10,"area":null},{"id":"2408.06849","title":"Causal Agent based on Large Language Model","created":"2024-08-13","cited_by_count":10,"area":"agents"},{"id":"2409.11724","title":"TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning","created":"2024-09-18","cited_by_count":10,"area":"lr"},{"id":"2505.15110","title":"RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals","created":"2025-05-21","cited_by_count":10,"area":"lr"},{"id":"2508.00217","title":"Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges","created":"2025-07-31","cited_by_count":10,"area":"mllm"},{"id":"2406.04113","title":"Uncovering Limitations of Large Language Models in Information Seeking from Tables","created":"2024-06-06","cited_by_count":9,"area":null},{"id":"2506.11684","title":"MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space","created":"2025-06-13","cited_by_count":9,"area":"lr"},{"id":"2601.17058","title":"Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs","created":"2026-01-22","cited_by_count":9,"area":null},{"id":"2408.08841","title":"FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats","created":"2024-08-16","cited_by_count":8,"area":"lr"},{"id":"2410.12164","title":"Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning","created":"2024-10-16","cited_by_count":8,"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":"2509.10697","title":"A Survey on Retrieval And Structuring Augmented Generation with Large Language Models","created":"2025-09-12","cited_by_count":8,"area":null},{"id":"2410.04269","title":"RoQLlama: A Lightweight Romanian Adapted Language Model","created":"2024-10-05","cited_by_count":7,"area":null},{"id":"2501.02152","title":"Table as Thought: Exploring Structured Thoughts in LLM Reasoning","created":"2025-01-04","cited_by_count":7,"area":"lr"},{"id":"2501.17767","title":"Hybrid Graphs for Table-and-Text based Question Answering using LLMs","created":"2025-01-29","cited_by_count":7,"area":null},{"id":"2502.11735","title":"MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables","created":"2025-02-17","cited_by_count":7,"area":"rag"},{"id":"2503.12345","title":"General Table Question Answering via Answer-Formula Joint Generation","created":"2025-03-16","cited_by_count":7,"area":null},{"id":"2506.04427","title":"Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance","created":"2025-06-04","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":"2409.18475","title":"Data Analysis in the Era of Generative AI","created":"2024-09-27","cited_by_count":6,"area":null},{"id":"2502.00329","title":"CoddLLM: Empowering Large Language Models for Data Analytics","created":"2025-02-01","cited_by_count":6,"area":null},{"id":"2502.17315","title":"HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization","created":"2025-02-24","cited_by_count":6,"area":"alignment"},{"id":"2504.09841","title":"StruPhantom: Evolutionary Injection Attacks on Black-Box Tabular Agents Powered by Large Language Models","created":"2025-04-14","cited_by_count":6,"area":"agents"},{"id":"2508.15809","title":"Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration","created":"2025-08-14","cited_by_count":6,"area":"agents"},{"id":"2604.21696","title":"Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks","created":"2026-04-23","cited_by_count":6,"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":"2510.06217","title":"TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning","created":"2025-10-07","cited_by_count":5,"area":"lr"},{"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":"2603.11339","title":"FinRule-Bench: A Benchmark for Joint Reasoning over Financial Tables and Principles","created":"2026-03-11","cited_by_count":5,"area":"lr"},{"id":"2405.16234","title":"Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities","created":"2024-05-25","cited_by_count":4,"area":"mllm"},{"id":"2501.16247","title":"Zero-Shot Decision Tree Construction via Large Language Models","created":"2025-01-27","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":"2503.21735","title":"GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics","created":"2025-03-27","cited_by_count":4,"area":"lr"},{"id":"2506.08646","title":"TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning","created":"2025-06-10","cited_by_count":4,"area":"synth"},{"id":"2604.03393","title":"TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering","created":"2026-04-03","cited_by_count":4,"area":"lr"},{"id":"2404.12580","title":"iTBLS: A Dataset of Interactive Conversations Over Tabular Information","created":"2024-04-19","cited_by_count":3,"area":null},{"id":"2409.12046","title":"Using Large Language Models to Generate Clinical Trial Tables and Figures","created":"2024-09-18","cited_by_count":3,"area":null},{"id":"2409.19573","title":"See then Tell: Enhancing Key Information Extraction with Vision Grounding","created":"2024-09-29","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":"2411.01093","title":"TabVer: Tabular Fact Verification with Natural Logic","created":"2024-11-02","cited_by_count":3,"area":"lr"},{"id":"2411.17110","title":"TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations","created":"2024-11-26","cited_by_count":3,"area":null},{"id":"2505.23667","title":"Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learning","created":"2025-05-29","cited_by_count":3,"area":"lr"},{"id":"2509.01312","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","created":"2025-09-01","cited_by_count":3,"area":"agents"},{"id":"2509.14671","title":"TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding","created":"2025-09-18","cited_by_count":3,"area":null},{"id":"2408.02907","title":"Leveraging Inter-Chunk Interactions for Enhanced Retrieval in Large Language Model-Based Question Answering","created":"2024-08-06","cited_by_count":2,"area":null},{"id":"2408.06423","title":"Evaluating LLMs on Entity Disambiguation in Tables","created":"2024-08-12","cited_by_count":2,"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":"2506.13328","title":"Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach","created":"2025-06-16","cited_by_count":2,"area":null},{"id":"2507.10281","title":"Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence","created":"2025-07-14","cited_by_count":2,"area":"agents"},{"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":"2508.18676","title":"Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding","created":"2025-08-26","cited_by_count":2,"area":"lr"},{"id":"2605.14465","title":"From Table to Cell: Attention for Better Reasoning with TABALIGN","created":"2026-05-14","cited_by_count":2,"area":"lr"},{"id":"2410.12878","title":"Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2505.10714","title":"GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?","created":"2025-05-15","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":"2505.21807","title":"TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction","created":"2025-05-27","cited_by_count":1,"area":"lr"},{"id":"2506.06371","title":"Relationship Detection on Tabular Data Using Statistical Analysis and Large Language Models","created":"2025-06-04","cited_by_count":1,"area":null},{"id":"2602.01969","title":"Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models","created":"2026-02-02","cited_by_count":1,"area":"lr"},{"id":"2602.07034","title":"ST-Raptor: An Agentic System for Semi-Structured Table QA","created":"2026-02-03","cited_by_count":1,"area":"agents"},{"id":"2602.09642","title":"MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering","created":"2026-02-10","cited_by_count":1,"area":"agents"}],"next_cursor":"100"}