arXiv AI August 18, 2026 By Amritansh Maurya, Navjot Singh, Mohammed Javed, Omar Moured

Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting

arXiv:2605. 20254v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, because Table Question-Answering (TQA) requires precise cell retrieval and multi-step structured reasoning.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 10

LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.

By Haonan Wang, Jiaxiang Liu, Yurong Liu, Austin Senna Wijaya, Tianle Zhou, Eden Wu, Yijia Chen, Wanting You, Reya Vir, Daniela Pinto, Grace Fan, Yusen Zhang, Juliana Freire, Eugene Wu

llms agents nlp benchmarks

arXiv AI

Jun 9

TQA-Bench: Evaluating LLMs for Multi-Table Question Answering

arXiv:2411. 19504v2 Announce Type: replace Abstract: The advance of large language models (LLMs) has unlocked great opportunities in complex multi-modal data management tasks, particularly in question answering (QA) over complicated multi-table relational data.

By Zipeng Qiu, Chenyue Li, You Peng, Guangxin He, Binhang Yuan, Chen Wang

llms nlp multimodal benchmarks

arXiv AI

Jul 22

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

arXiv:2506. 18421v3 Announce Type: replace-cross Abstract: The majority of data in businesses and industries is stored in tables, databases, and data warehouses.

By Ce Li, Xiaofan Liu, Zhiyan Song, Ce Chi, Boshen Shi, Chen Zhao, Guanguang Chang, Zhendong Wang, Kexin Yang, Xing Wang, Chao Deng, Junlan Feng

llms benchmarks

arXiv AI

Jul 28

Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering

arXiv:2607. 22633v1 Announce Type: new Abstract: Table Question Answering (TableQA) aims to reason over tables to answer user queries.

By Guixin Su, Qiankun Pi, Mayi Xu, Wenli Li, Ming Zhong, Yuanyuan Zhu, Jiawei Jiang, Tieyun Qian

llms nlp efficiency

arXiv AI

Jun 18

TopBench: A Benchmark for Implicit Predictive Reasoning in Tabular Question Answering

arXiv:2604. 28076v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregation.

By An-Yang Ji, Jun-Peng Jiang, De-Chuan Zhan, Han-Jia Ye

llms agents nlp benchmarks

arXiv AI

Jul 28

TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning

arXiv:2509. 06278v4 Announce Type: replace Abstract: Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations.

By Chuang Jiang, Mingyue Cheng, Xiaoyu Tao, Qingyang Mao, Jie Ouyang, Qi Liu

llms agents reinforcement-learning fine-tuning benchmarks

Related stories

LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

TQA-Bench: Evaluating LLMs for Multi-Table Question Answering

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering

TopBench: A Benchmark for Implicit Predictive Reasoning in Tabular Question Answering

TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning