M3TQA: Massively Multilingual Multitask Table Question Answering

• URL: http://arxiv.org/abs/2508.16265v1
• Date: Fri, 22 Aug 2025 09:57:40 GMT
• Title: M3TQA: Massively Multilingual Multitask Table Question Answering
• Authors: Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Xiangyuan Guan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li,
• Abstract summary: m3TQA-Instruct is a large-scale benchmark spanning 97 languages across diverse language families.<n>We construct m3TQA by curating 50 real-world tables in Chinese and English, then applying a robust six-step translation pipeline powered by DeepSeek and GPT-4o.<n>The benchmark includes 2,916 professionally annotated question-answering pairs across four tasks designed to evaluate nuanced table reasoning capabilities.
• Score: 39.99483693397598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages [33.450081592217074]
  We introduce MuBench, a benchmark covering 61 languages and evaluating a broad range of capabilities.<n>We evaluate several state-of-the-art multilingual LLMs and find notable gaps between claimed and actual language coverage.
  arXiv  Detail & Related papers  (2025-06-24T09:53:00Z)
• MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application [118.63802040274999]
  MultiFinBen is the first expert-annotated multilingual (five languages) and multimodal benchmark for evaluating LLMs in realistic financial contexts.<n>Financial reasoning tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents.<n> evaluating 21 leading LLMs shows that even frontier multimodal models like GPT-4o achieve only 46.01% overall, stronger on vision and audio but dropping sharply in multilingual settings.
  arXiv  Detail & Related papers  (2025-06-16T22:01:49Z)
• XIFBench: Evaluating Large Language Models on Multilingual Instruction Following [59.549015333755186]
  Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications.<n>Existing evaluations lack fine-grained constraint analysis across diverse linguistic contexts.<n>We introduce XIFBench, a comprehensive benchmark for evaluating multilingual instruction-following abilities of LLMs.
  arXiv  Detail & Related papers  (2025-03-10T17:07:52Z)
• Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following [51.18383180774354]
  We introduce Multi-IF, a new benchmark designed to assess Large Language Models' proficiency in following multi-turn and multilingual instructions. Our evaluation of 14 state-of-the-art LLMs on Multi-IF reveals that it presents a significantly more challenging task than existing benchmarks. languages with non-Latin scripts (Hindi, Russian, and Chinese) generally exhibit higher error rates, suggesting potential limitations in the models' multilingual capabilities.
  arXiv  Detail & Related papers  (2024-10-21T00:59:47Z)
• INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages [25.402797722575805]
  Indic QA Benchmark is a dataset for context grounded question answering in 11 major Indian languages.<n> Evaluations revealed weak performance in low resource languages due to a strong English language bias in their training data.<n>We also investigated the Translate Test paradigm,where inputs are translated to English for processing and the results are translated back into the source language for output.
  arXiv  Detail & Related papers  (2024-07-18T13:57:16Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
  Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
  arXiv  Detail & Related papers  (2023-06-20T08:27:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.