Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world. The 141 language varieties in Global PIQA cover five continents, 19 language families, and 24 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. In the parallel split, we translate more "culturally agnostic" commonsense reasoning questions into 131 language varieties, for direct cross-lingual comparisons. In both splits, all examples have been verified by native speakers of the languages. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (e.g. up to a 68% accuracy gap between languages in the parallel split). Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement in LLMs, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 27 of its 109 references that are in the map. First posted on arXiv 28 Oct 2025.
Builds on
27 of 109 references.
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Other references · 26
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Built on by
Citing papers per year: 2026: 8.
8 papers.
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- 2026ADAGE: A Language-Agnostic Pipeline for Analogical Reasoning Evaluation25 Jul 2026
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- 2026Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling28 Apr 2026
- 2026Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch16 Apr 2026
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