Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+
  Languages and Cultures
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 335
researchers from 65 countries around the world. The 116 language varieties in
Global PIQA cover five continents, 14 language families, and 23 writing
systems. In the non-parallel split of Global PIQA, over 50% of examples
reference local foods, customs, traditions, or other culturally-specific
elements. We find that state-of-the-art LLMs perform well on Global PIQA in
aggregate, but they exhibit weaker performance in lower-resource languages (up
to a 37% accuracy gap, despite random chance at 50%). Open models generally
perform worse than proprietary models. Global PIQA highlights that in many
languages and cultures, everyday knowledge remains an area for improvement,
alongside more widely-discussed capabilities such as complex reasoning and
expert knowledge. Beyond its uses for LLM evaluation, we hope that Global PIQA
provides a glimpse into the wide diversity of cultures in which human language
is embedded.
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Sander Land
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Sultan AlRashed
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Zaid Alyafeai
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Zhengyang Shan
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David Ifeoluwa Adelani
Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+
  Languages and Cultures
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 335
researchers from 65 countries around the world. The 116 language varieties in
Global PIQA cover five continents, 14 language families, and 23 writing
systems. In the non-parallel split of Global PIQA, over 50% of examples
reference local foods, customs, traditions, or other culturally-specific
elements. We find that state-of-the-art LLMs perform well on Global PIQA in
aggregate, but they exhibit weaker performance in lower-resource languages (up
to a 37% accuracy gap, despite random chance at 50%). Open models generally
perform worse than proprietary models. Global PIQA highlights that in many
languages and cultures, everyday knowledge remains an area for improvement,
alongside more widely-discussed capabilities such as complex reasoning and
expert knowledge. Beyond its uses for LLM evaluation, we hope that Global PIQA
provides a glimpse into the wide diversity of cultures in which human language
is embedded.
Global PIQA is a multilingual commonsense reasoning benchmark that highlights performance gaps of large language models across different cultures and languages.
large language models LLMs commonsense reasoning Global PIQA language varieties culturally-specific elements lower-resource languages open models proprietary models