{"total":66,"rows":[{"id":"2505.05568","title":"Griffin: Towards a Graph-Centric Relational Database Foundation Model","created":"2025-05-08","cited_by_count":47,"area":null},{"id":"2506.09003","title":"SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner","created":"2025-06-10","cited_by_count":20,"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":"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":"2508.21741","title":"Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance","created":"2025-08-29","cited_by_count":13,"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":"2510.23045","title":"A Survey of AI Scientists","created":"2025-10-27","cited_by_count":11,"area":null},{"id":"2410.17787","title":"Large Language Models Engineer Too Many Simple Features For Tabular Data","created":"2024-10-23","cited_by_count":10,"area":null},{"id":"2412.11757","title":"SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types","created":"2024-12-16","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":"2508.05201","title":"FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance","created":"2025-08-07","cited_by_count":8,"area":null},{"id":"2509.02033","title":"StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching","created":"2025-09-02","cited_by_count":8,"area":null},{"id":"2601.03851","title":"Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search","created":"2026-01-07","cited_by_count":8,"area":null},{"id":"2604.10079","title":"Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models","created":"2026-04-11","cited_by_count":8,"area":null},{"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":"2506.19794","title":"Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study","created":"2025-06-24","cited_by_count":7,"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":"2601.04202","title":"TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation","created":"2025-12-05","cited_by_count":6,"area":null},{"id":"2505.15874","title":"Text-to-Pipeline: Bridging Natural Language and Data Preparation Pipelines","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2507.22462","title":"IFEvalCode: Controlled Code Generation","created":"2025-07-30","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":"2601.17777","title":"DPI: Exploiting Parameter Heterogeneity for Interference-Free Fine-Tuning","created":"2026-01-25","cited_by_count":5,"area":null},{"id":"2603.09151","title":"Deep Tabular Research via Continual Experience-Driven Execution","created":"2026-03-10","cited_by_count":5,"area":null},{"id":"2412.07629","title":"Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering","created":"2024-12-10","cited_by_count":3,"area":null},{"id":"2502.14848","title":"GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks","created":"2025-02-20","cited_by_count":3,"area":null},{"id":"2508.16265","title":"M3TQA: Massively Multilingual Multitask Table Question Answering","created":"2025-08-22","cited_by_count":3,"area":null},{"id":"2510.13835","title":"ConDABench: Interactive Evaluation of Language Models for Data Analysis","created":"2025-10-10","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":"2511.10659","title":"Information Extraction From Fiscal Documents Using LLMs","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2603.29112","title":"GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification","created":"2026-03-31","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":"2505.15094","title":"SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models","created":"2025-05-21","cited_by_count":2,"area":null},{"id":"2604.14010","title":"Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning","created":"2026-04-15","cited_by_count":2,"area":null},{"id":"2604.22239","title":"Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA","created":"2026-04-24","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":"2506.20747","title":"Towards Probabilistic Question Answering Over Tabular Data","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2507.06138","title":"Coding Triangle: How Does Large Language Model Understand Code?","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2507.10934","title":"Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models","created":"2025-07-15","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":"2511.17238","title":"Lost in Translation and Noise: A Deep Dive into the Failure Modes of VLMs on Real-World Tables","created":"2025-11-21","cited_by_count":1,"area":null},{"id":"2604.13201","title":"InfiniteScienceGym: An Unbounded, Procedurally-Generated Benchmark for Scientific Analysis","created":"2026-04-14","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":"2606.32029","title":"When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2607.06482","title":"Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities","created":"2026-07-07","cited_by_count":1,"area":null},{"id":"2505.06605","title":"Using External knowledge to Enhanced PLM for Semantic Matching","created":"2025-05-10","cited_by_count":0,"area":null},{"id":"2505.21409","title":"RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models","created":"2025-05-27","cited_by_count":0,"area":null},{"id":"2601.03137","title":"Accurate Table Question Answering with Accessible LLMs","created":"2026-01-06","cited_by_count":0,"area":null},{"id":"2602.08548","title":"How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2602.22721","title":"Replacing Multi-Step Assembly of Data Preparation Pipelines with One-Step LLM Pipeline Generation for Table QA","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2603.02212","title":"GLEAN: Grounded Lightweight Evaluation Anchors for Contamination-Aware Tabular Reasoning","created":"2026-01-22","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":"2603.17191","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":"2604.12491","title":"Calibrated Confidence Estimation for Tabular Question Answering","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2604.17051","title":"Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization","created":"2026-04-18","cited_by_count":0,"area":null},{"id":"2604.17944","title":"ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2604.21120","title":"TabSHAP","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.20254","title":"Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.24417","title":"Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2605.31550","title":"Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.02170","title":"CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.05181","title":"Multi-Granularity Reasoning for Natural Language Inference","created":"2026-04-18","cited_by_count":0,"area":null},{"id":"2608.00018","title":"CITBench: A Comprehensive Benchmark for Interactive Tabular Data Processing with LLMs","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2609.03395","title":"TabScope: Question-Adaptive Scope Selection for Table Question Answering","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2609.34228","title":"SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.03525","title":"Structured Composition of Verifiable Atomic Insights for Table-to-Report Generation","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}