If I Could Turn Back Time: Temporal Reframing as a Historical Reasoning Task for LLMs
Authors:
Lars Bungum,
Charles Yijia Huang,
Abeer Kashar
Abstract:
In this study, we experiment with the ability of LLMs to do temporal reasoning. Using a Norwegian book from 1940 containing trivia questions, we prompt the LLMs to answer the questions as if it were 1940. We also pose the questions in both English and Norwegian. Correct answers are often presented as sentences, and grading is done by means of LLM-as-judge, with sampled checks by a native speaker.…
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In this study, we experiment with the ability of LLMs to do temporal reasoning. Using a Norwegian book from 1940 containing trivia questions, we prompt the LLMs to answer the questions as if it were 1940. We also pose the questions in both English and Norwegian. Correct answers are often presented as sentences, and grading is done by means of LLM-as-judge, with sampled checks by a native speaker. Prompting in English consistently gave better results than in Norwegian, an unexpected result. In contrast, using larger LLMs improved results. We tested the DeepSeek-R1, Gemma3, Qwen3, and Llama3.1 model families, and also the largest available LLM especially crafted for Norwegian.
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Submitted 6 November, 2025;
originally announced November 2025.
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Authors:
Tyler A. Chang,
Catherine Arnett,
Abdelrahman Sadallah,
Abdelrahman Eldesokey,
Abeer Kashar,
Abolade Daud,
Abosede Grace Olanihun,
Adamu Labaran Mohammed,
Adeyemi Praise,
Adhikarimayum Meerajita Sharma,
Aditi Gupta,
Adril Putra Merin,
Adwoa Bremang,
Afitab Iyigun,
Afonso Simplício,
Ahmed Essouaied,
Aicha Chorana,
Akhil Eppa,
Akintunde Oladipo,
Akriti Kuri,
Akshay Ramesh,
Aleksei Dorkin,
Alfred Malengo Kondoro,
Alham Fikri Aji,
Ali Eren Çetintaş
, et al. (355 additional authors not shown)
Abstract:
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 cov…
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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.
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Submitted 29 May, 2026; v1 submitted 28 October, 2025;
originally announced October 2025.