Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling (2026.acl-long)
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Alaa Elsetohy, Sama Hadhoud, Haryo Akbarianto Wibowo, Chenxi Whitehouse, Genta Indra Winata, Fajri Koto, Alham Fikri Aji
| Challenge: | Existing benchmarks test reasoning over culturally grounded premises, but translation-parallel benchmarks inherit English-centric scenarios. |
| Approach: | They propose a template-first benchmark that factorizes reasoning type and cultural aspect across question languages. |
| Outcome: | The proposed benchmark factorizes reasoning type and cultural aspect across question languages. |
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Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov, Fajri Koto
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| Challenge: | Existing models show low performance for lesser resourced languages, but they can achieve surprising performance on complex reasoning tasks in natural language processing (NLP). |
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MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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| Challenge: | a benchmark for multilingual complex reasoning spans 374 high-quality math problems across 10 typologically diverse languages. |
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Ruiyang Xu, Jialun Cao, Yaojie Lu, Ming Wen, Hongyu Lin, Xianpei Han, Ben He, Shing-Chi Cheung, Le Sun
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William Gantt, Shabnam Behzad, Hannah An, Yunmo Chen, Aaron White, Benjamin Van Durme, Mahsa Yarmohammadi
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| Challenge: | Large Language Models exhibit a specific cultural bias, neglecting values and differences of low-resource regions. |
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| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
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NativQA: Multilingual Culturally-Aligned Natural Query for LLMs (2025.findings-acl)
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Md. Arid Hasan, Maram Hasanain, Fatema Ahmad, Sahinur Rahman Laskar, Sunaya Upadhyay, Vrunda N Sukhadia, Mucahid Kutlu, Shammur Absar Chowdhury, Firoj Alam
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