arXiv AI July 22, 2026 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

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

Read the original on arXiv AI →

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

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

arXiv AI

Jul 14

ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm

arXiv:2607. 11207v1 Announce Type: cross Abstract: Table-based reasoning with large language models (LLMs), which requires reasoning based on natural language questions and structured tabular data, has gained widespread attention.

By Pei Guo, Enjie Liu, Yunzhi Tan, Mochi Gao, Jianxin Zhang, Ruichao Zhong, Juntao Li, Bo Hu, Zang Li

llms data-engineering

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

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 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 23

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

arXiv:2607. 19847v1 Announce Type: cross Abstract: Predicting missing cell values in tabular data is a fundamental problem in data cleaning.

By Yurong Liu, Yeye He, Haoyu Dong, Junjie Xing, Shi Han, Dongmei Zhang, Surajit Chaudhuri

llms benchmarks

arXiv AI

Jul 8

Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities

arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.

By So Hasegawa, Shailaja Keyur Sampat, Lei Liu, Wei-Peng Chen

llms agents benchmarks

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ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm

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

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

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

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities