arXiv AI June 9, 2026 By Zipeng Qiu, Chenyue Li, You Peng, Guangxin He, Binhang Yuan, Chen Wang

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

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

Aug 18

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

Sep 7

TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation

TeleTables is a benchmark that evaluates large language models on interpreting telecom tables from 3GPP specifications. It contains 2,220 tables in four formats and 500 human‑verified multiple‑choice questions that range from simple retrieval to multi‑step reasoning. Tests on 20 open‑weight LLMs show that closed‑book performance is limited by domain knowledge, while providing the table as context yields high accuracy that still drops with deeper reasoning, evidence scope, and structural complexity.

By Anas Ezzakri, Nicola Piovesan, Mohamed Sana, Antonio De Domenico, Fadhel Ayed, Haozhe Zhang

llms multimodal benchmarks

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 Machine Learning

Sep 23

Discovery-Driven Integration of Disjoint Tables via Text

arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit att...

By Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal

llms multimodal benchmarks

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

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

Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting

TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation

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

Discovery-Driven Integration of Disjoint Tables via Text