Table QA

QA: Question Answering

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Understanding Errors in LLM-Based Question Answering over Imperfect Tables
Oct 3, 2026Baowen Zhang, Wei Fan, Ruman Wang +1Table QA Language Model Error Detection
University of Wisconsin–Madison · University of Auckland, Auckland, New Zealand · Liaoning Provincial People’s Hospital +1
From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verification
Sep 21, 2026Mai Mohamed Eida, Gunjan Anand, Ayush Singh +1Table QA LLM Evaluation
University of Illinois Urbana Champaign
Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure
Sep 17, 2026Zofia SmoleńDocument Chunking Table QA
Systems Research Institute, Polish Academy of Sciences Warsaw, Poland
Tables Decoded: DELTA for Structure, TARQA for Understanding
Sep 15, 2026Jahanvi Rajput, Dhruv Kudale, Saikiran Kasturi +2Table QA Table Structure Recognition
Indian Institute of Technology Bombay · IIT Bombay · BharatGen
DeepTable: Structural Attention Biases and Tree Path Encoding for Hierarchical Table Understanding
Sep 7, 2026Jyun-Ying Yen, Cheng-Kuan Lin, Yu-Chee TsengTable QA Table Structure Recognition
Department of Computer Science National Yang Ming Chiao Tung University, Taiwan
Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?
Sep 7, 2026Daniel Gomm, Maarten de Rijke, Madelon HulsebosTable QA
Centrum Wiskunde & Informatica · University of Amsterdam
TabScope: Question-Adaptive Scope Selection for Table Question Answering
Sep 3, 2026Yuxiang Wang, Junhao Gan, Jianzhong QiTable QA
The University of Melbourne
H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning
Sep 1, 2026Jia Ling, Yangfan Wang, Chen Tang +4Table QA Hierarchical Representation Learning
Harbin Institute of Technology · AI Research Center, Midea Group (Shanghai) Co., Ltd. · Changchun University of Science and Technology +1
TabRank: Chain-of-Thought Distillation for Table Re-Rankers
Jul 28, 2026Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao +2Table QA CoT Distillation
1Arizona State University · 2Rensselaer Polytechnic Institute · 3Scale AI
Semantically Similar, Yet Not Answerable: Diagnosing the Semantic-Answerability Gap in Table RAG
Jul 20, 2026Jiaming Tian, Liyao Li, Wentao Ye +6Table QA Retrieval-Augmented Generation
Zhejiang University · Bank of Hangzhou Co., Ltd. · Zhejiang Lab
Gold-Guided Programmatic Distillation for Financial Reasoning over Hybrid Tables and Text
Jul 16, 2026Yun Dong, Erica Zhao, Elana ChenTable QA Financial QA
College of Computing · Georgia Institute of Technology · Department of Management Science & Engineering +2
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
Jul 7, 2026So Hasegawa, Shailaja Keyur Sampat, Lei Liu +1Table QA LLM Evaluation
Fujitsu Research of America, Santa Clara CA 95054, USA
Latent Bridges for Multi-Table Question Answering
Jun 27, 2026Simone Varriale, Tamara Cucumides, Floris Geerts +1Table QA LLM Grounding
1EURECOM · University of Antwerp
Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering
Jun 20, 2026Guixin Su, Qiankun Pi, Mayi Xu +5Table QA
School of Computer Science, Wuhan University, Wuhan, China
MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning
Jun 10, 2026Abdelrahman Abdallah, AbdelRahim A. Elmadany, Sameh Al Natour +3Table QA LLM Answer Verification
University of Innsbruck · University of British Columbia · λCanada Research Chair in NLP and ML +1
TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs
Jun 8, 2026Momina Ahsan, Sarfraz Ahmad, Ming Shan Hee +2Document Understanding Table QA
1Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · 2Singapore University of Technology and Design (SUTD)
CRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification
Jun 5, 2026Chenshuo Pan, Yu Zhao, Jie Zhang +7Table QA Claim Verification
Xingchen AGI Lab,China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd
Synthetic Contrastive Reasoning for Multi-Table Q&A
Jun 3, 2026Ankit Pratap Singh, Xin Su, Phillip HowardTable QA Synthetic Data Generation
Iowa State University · Thoughtworks
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
Jun 1, 2026Zhensheng Wang, Xiaole Liu, Wenmian Yang +3Table QA Time Series Reasoning
School of Artificial Intelligence, Beijing Normal University, Beijing, PR China · Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, PR China · Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai, PR China +1
CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning
Jun 1, 2026Chengtao Gan, Zhiqiang Liu, Long Jin +3Table QA LLM-Based Program Synthesis
Zhejiang University · JIUTIAN Research, Beijing, China · Ant Group +1
HakushoBench: A Japanese Chart and Table VQA Benchmark from Governmental White Papers
May 31, 2026Issa Sugiura, Shuhei Kurita, Yusuke Oda +1VLM Evaluation Data Visualization
Institute of Science Tokyo · NII LLMC · NII
Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models
May 29, 2026Yibin Zhao, Fangxin Shang, Dingrui Yang +1Table QA Table Structure Recognition
Taiyuan University of Technology · AI Lab, Qifu Technology, Beijing, China · AI Lab, Greensea Technology, Shenzhen, China
Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
May 18, 2026Amritansh Maurya, Navjot Singh, Mohammed Javed +1Table QA Structured Prompting
Vision Intelligence Lab, IIIT Allahabad, Prayagraj, India · iMeDIA Lab, IIIT Allahabad, Prayagraj, India · CV:HCI Lab, Karlsruhe Institute of Technology, Karlsruhe, Germany
From Table to Cell: Attention for Better Reasoning with TABALIGN
May 14, 2026Tung Sum Thomas Kwok, Zeyong Zhang, Xinyu Wang +6Table QA Structured Reasoning
University of California, Los Angeles · SimpleWay · New Jersey Institute of Technology +4
Can Language Models Analyze Data? Evaluating Large Language Models for Question Answering over Datasets
May 11, 2026Andreas Xenofontos, Pavlos FafaliosTable QA LLM Evaluation
School of Production Engineering and Management, Technical University of Crete, Greece · Institute of Computer Science, Foundation for Research and Technology - Hellas, Greece.
FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
May 2, 2026Zebin Guo, Weidong Geng, Ruichen MaoMultimodal RAG Table QA
Georgia Institute of Technology Atlanta, Georgia, USA · Zhejiang University Hangzhou, Zhejiang, China · Zhejiang Lab Hangzhou, Zhejiang, China
WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild
May 1, 2026Junzhe Huang, Xiaoxiao Sun, Yan Yang +6Visual Question Answering Table QA
The University of Queensland · Stanford University · The Australian National University +3
The Power of Order: Fooling LLMs with Adversarial Table Permutations
May 1, 2026Xinshuai Dong, Haifeng Chen, Xuyuan Liu +5Table QA Adversarial Attacks on LLMs
CMU & NEC Labs · NEC Labs · Dartmouth College & NEC Labs +1
RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners
Apr 30, 2026Jugal Gajjar, Kamalasankari SubramaniakuppusamyTable QA LLM Grounding
Department of Computer Science, The George Washington University, USA
TopBench: A Benchmark for Implicit Predictive Reasoning in Tabular Question Answering
Apr 30, 2026An-Yang Ji, Jun-Peng Jiang, De-Chuan Zhan +1Table QA LLM Evaluation
School of Artificial Intelligence, Nanjing University, China · National Key Laboratory for Novel Software Technology, Nanjing University, China.
SCOPE:Planning for Hybrid Querying over Clinical Trial Data
Apr 28, 2026Suparno Roy Chowdhury, Manan Roy Choudhury, Tejas Anvekar +5Table QA Healthcare
1Arizona State University · 2Mayo Clinic
Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning
Apr 23, 2026Hanjun Cho, Gahyun Yoo, Hanseong Kim +1Table QA Self-Supervised Pre-Training
Seoul National University · Soongsil University
DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates
Apr 17, 2026Laziz Hamdi, Amine Tamasna, Thierry PaquetTable QA Table Structure Recognition
LITIS, Normandy, France · Malakoff Humanis, Paris, France
Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models
Feb 2, 2026Bin Cao, Huixian Lu, Chenwen Ma +3Table QA Table Structure Recognition
Zhejiang University of Technology, China · Zhejiang Key Laboratory of Visual Information Intelligent Processing, China · University of Aberdeen, UK +1
Evidence-Guided Schema Normalization for Temporal Tabular Reasoning
Nov 29, 2025Ashish Thanga, Vibhu Dixit, Abhilash Shankarampeta +1KG Construction Table QA
Arizona State University · UC San Diego

Oct 3, 2026, cs.CLJ/K move · Enter open · S save

Understanding Errors in LLM-Based Question Answering over Imperfect Tables

Baowen Zhang, Wei Fan, Ruman Wang, Hangting Ye

University of Wisconsin–Madison · University of Auckland, Auckland, New Zealand · Liaoning Provincial People’s Hospital+1

Table QA Language Model Error Detection Tabular Anomaly Detection LLM Reliability

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Answering questions over imperfect tables requires handling errors that can affect the answer. We investigate two challenges for large language models (LLMs): whether error discovery depends on where errors appear in a table, and whether providing their locations is sufficient for accurate question answering (QA). Using human-reviewed instances from RADAR-T, we conduct controlled studies across three LLMs by varying row order and comparing original, error-marked, and repaired tables. First, reordering rows changes error discovery even when the table contents and gold answer remain unchanged. During direct inspection, LLMs are more likely to discover all rows containing relevant errors when these rows appear later in the table or are grouped more closely together. Second, providing verified error locations alone is insufficient for accurate QA: with code execution, accuracy on repaired tables exceeds that on error-marked tables by 39.0-59.1 percentage points across the three LLMs. As a practical application of these findings, we combine error discovery across shuffled table views with explicit guidance for verifying and handling the reported errors in a simple workflow, Geometry-Balanced Discovery and Intervention (GBDI). On RADAR-T, GBDI improves QA accuracy by 3.8-18.5 percentage points over a code-agent baseline across five LLMs (paired 95% confidence intervals exclude zero for four), at the cost of additional inference. These results highlight the importance of both reliable error discovery and effective error handling in QA over imperfect tables. Code is available at https://github.com/645-t/GBDI-ICLR-2027.

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