arXiv AI

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.

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

arXiv AI

1d ago

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.

By Amritansh Maurya, Navjot Singh, Mohammed Javed, Omar Moured

llms nlp fine-tuning benchmarks

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 Machine Learning

Aug 3

TELLER: Dual-Path Iterative Preference Optimization for Table Entity Linking

arXiv:2607. 28680v1 Announce Type: cross Abstract: Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities.

By Yixin Peng, Kehao Li, Stefan Decker

fine-tuning safety

arXiv AI

Jul 1

When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors

arXiv:2606. 32029v1 Announce Type: cross Abstract: While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.

By Yuqing Yang, Qi Zhu, Zhen Han, Boran Han, Zhengyuan Shen, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala

llms

arXiv AI

Jul 29

TabRank: Chain-of-Thought Distillation for Table Re-Rankers

arXiv:2607. 25182v1 Announce Type: cross Abstract: The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval.

By Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao, Soham Dan, Vivek Gupta

llms rag efficiency benchmarks

arXiv AI

Jun 30

Latent Bridges for Multi-Table Question Answering

arXiv:2606. 28916v1 Announce Type: cross Abstract: We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering.

By Simone Varriale, Tamara Cucumides, Floris Geerts, Paolo Papotti

llms nlp

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