M3TQA: Massively Multilingual Multitask Table Question Answering

TL;DR

This paper introduces m3TQA, a large-scale multilingual table question answering benchmark covering 97 languages, designed to evaluate and improve cross-lingual table understanding with high-quality translations and diverse tasks.

Contribution

It presents a comprehensive multilingual benchmark with 97 languages, a robust translation pipeline, and insights into cross-lingual generalization for table QA.

Findings

Synthetic QA data improves low-resource language performance.

High translation fidelity achieved with median BLEU score of 60.19.

Benchmark sets new standards for multilingual table understanding.

Abstract

Tabular data is a fundamental component of real-world information systems, yet most research in table understanding remains confined to English, leaving multilingual comprehension significantly underexplored. Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. To address these limitations, we introduce a comprehensive framework for massively multilingual multitask table question answering, featuring m3TQA-Instruct, a large-scale benchmark spanning 97 languages across diverse language families, including underrepresented and low-resource languages. We construct m3TQA by curating 50 real-world tables in Chinese and English, then applying a robust six-step LLM-based translation pipeline powered by DeepSeek and GPT-4o, achieving high translation fidelity with a median BLEU score of 60.19 as validated through back-translation. The benchmark includes 2,916 professionally annotated question-answering pairs across four tasks designed to evaluate nuanced table reasoning capabilities. Experiments on state-of-the-art LLMs reveal critical insights into cross-lingual generalization, demonstrating that synthetically generated, unannotated QA data can significantly boost performance, particularly for low-resource languages. M3T-Bench establishes a new standard for multilingual table understanding, providing both a challenging evaluation platform and a scalable methodology for future research.