Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures

• URL: http://arxiv.org/abs/2510.24081v1
• Date: Tue, 28 Oct 2025 05:46:25 GMT
• Title: Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures
• Authors: Tyler A. Chang, Catherine Arnett, Abdelrahman Eldesokey, Abdelrahman Sadallah, Abeer Kashar, Abolade Daud, Abosede Grace Olanihun, Adamu Labaran Mohammed, Adeyemi Praise, Adhikarinayum Meerajita Sharma, Aditi Gupta, Afitab Iyigun, Afonso Simplício, Ahmed Essouaied, Aicha Chorana, Akhil Eppa, Akintunde Oladipo, Akshay Ramesh, Aleksei Dorkin, Alfred Malengo Kondoro, Alham Fikri Aji, Ali Eren Çetintaş, Allan Hanbury, Alou Dembele, Alp Niksarli, Álvaro Arroyo, Amin Bajand, Amol Khanna, Ana Chkhaidze, Ana Condez, Andiswa Mkhonto, Andrew Hoblitzell, Andrew Tran, Angelos Poulis, Anirban Majumder, Anna Vacalopoulou, Annette Kuuipolani Kanahele Wong, Annika Simonsen, Anton Kovalev, Ashvanth. S, Ayodeji Joseph Lana, Barkin Kinay, Bashar Alhafni, Benedict Cibalinda Busole, Bernard Ghanem, Bharti Nathani, Biljana Stojanovska Đurić, Bola Agbonile, Bragi Bergsson, Bruce Torres Fischer, Burak Tutar, Burcu Alakuş Çınar, Cade J. Kanoniakapueo Kane, Can Udomcharoenchaikit, Catherine Arnett, Chadi Helwe, Chaithra Reddy Nerella, Chen Cecilia Liu, Chiamaka Glory Nwokolo, Cristina España-Bonet, Cynthia Amol, DaeYeop Lee, Dana Arad, Daniil Dzenhaliou, Daria Pugacheva, Dasol Choi, Daud Abolade, David Liu, David Semedo, Deborah Popoola, Deividas Mataciunas, Delphine Nyaboke, Dhyuthy Krishna Kumar, Diogo Glória-Silva, Diogo Tavares, Divyanshu Goyal, DongGeon Lee, Ebele Nwamaka Anajemba, Egonu Ngozi Grace, Elena Mickel, Elena Tutubalina, Elias Herranen, Emile Anand, Emmanuel Habumuremyi, Emuobonuvie Maria Ajiboye, Eryawan Presma Yulianrifat, Esther Adenuga, Ewa Rudnicka, Faith Olabisi Itiola, Faran Taimoor Butt, Fathima Thekkekara, Fatima Haouari, Filbert Aurelian Tjiaranata, Firas Laakom, Francesca Grasso, Francesco Orabona, Francesco Periti, Gbenga Kayode Solomon, Gia Nghia Ngo, Gloria Udhehdhe-oze, Gonçalo Martins, Gopi Naga Sai Ram Challagolla, Guijin Son, Gulnaz Abdykadyrova, Hafsteinn Einarsson, Hai Hu, Hamidreza Saffari, Hamza Zaidi, Haopeng Zhang, Harethah Abu Shairah, Harry Vuong, Hele-Andra Kuulmets, Houda Bouamor, Hwanjo Yu, Iben Nyholm Debess, İbrahim Ethem Deveci, Ikhlasul Akmal Hanif, Ikhyun Cho, Inês Calvo, Inês Vieira, Isaac Manzi, Ismail Daud, Itay Itzhak, Iuliia, Alekseenko, Ivan Belashkin, Ivan Spada, Ivan Zhelyazkov, Jacob Brinton, Jafar Isbarov, Jaka Čibej, Jan Čuhel, Jan Kocoń, Jauza Akbar Krito, Jebish Purbey, Jennifer Mickel, Jennifer Za, Jenny Kunz, Jihae Jeong, Jimena Tena Dávalos, Jinu Lee, João Magalhães, John Yi, Jongin Kim, Joseph Chataignon, Joseph Marvin Imperial, Jubeerathan Thevakumar, Judith Land, Junchen Jiang, Jungwhan Kim, Kairit Sirts, Kamesh R, Kamesh V, Kanda Patrick Tshinu, Kätriin Kukk, Kaustubh Ponkshe, Kavsar Huseynova, Ke He, Kelly Buchanan, Kengatharaiyer Sarveswaran, Kerem Zaman, Khalil Mrini, Kian Kyars, Krister Kruusmaa, Kusum Chouhan, Lainitha Krishnakumar, Laura Castro Sánchez, Laura Porrino Moscoso, Leshem Choshen, Levent Sencan, Lilja Øvrelid, Lisa Alazraki, Lovina Ehimen-Ugbede, Luheerathan Thevakumar, Luxshan Thavarasa, Mahnoor Malik, Mamadou K. Keita, Mansi Jangid, Marco De Santis, Marcos García, Marek Suppa, Mariam D'Ciofalo, Marii Ojastu, Maryam Sikander, Mausami Narayan, Maximos Skandalis, Mehak Mehak, Mehmet İlteriş Bozkurt, Melaku Bayu Workie, Menan Velayuthan, Michael Leventhal, Michał Marcińczuk, Mirna Potočnjak, Mohammadamin Shafiei, Mridul Sharma, Mrityunjaya Indoria, Muhammad Ravi Shulthan Habibi, Murat Kolić, Nada Galant, Naphat Permpredanun, Narada Maugin, Nicholas Kluge Corrêa, Nikola Ljubešić, Nirmal Thomas, Nisansa de Silva, Nisheeth Joshi, Nitish Ponkshe, Nizar Habash, Nneoma C. Udeze, Noel Thomas, Noémi Ligeti-Nagy, Nouhoum Coulibaly, Nsengiyumva Faustin, Odunayo Kareemat Buliaminu, Odunayo Ogundepo, Oghojafor Godswill Fejiro, Ogundipe Blessing Funmilola, Okechukwu God'spraise, Olanrewaju Samuel, Olaoye Deborah Oluwaseun, Olasoji Akindejoye, Olga Popova, Olga Snissarenko, Onyinye Anulika Chiemezie, Orkun Kinay, Osman Tursun, Owoeye Tobiloba Moses, Oyelade Oluwafemi Joshua, Oyesanmi Fiyinfoluwa, Pablo Gamallo, Pablo Rodríguez Fernández, Palak Arora, Pedro Valente, Peter Rupnik, Philip Oghenesuowho Ekiugbo, Pramit Sahoo, Prokopis Prokopidis, Pua Niau-Puhipau, Quadri Yahya, Rachele Mignone, Raghav Singhal, Ram Mohan Rao Kadiyala, Raphael Merx, Rapheal Afolayan, Ratnavel Rajalakshmi, Rishav Ghosh, Romina Oji, Ron Kekeha Solis, Rui Guerra, Rushikesh Zawar, Sa'ad Nasir Bashir, Saeed Alzaabi, Sahil Sandeep, Sai Pavan Batchu, SaiSandeep Kantareddy, Salsabila Zahirah Pranida, Sam Buchanan, Samuel Rutunda, Sander Land, Sarah Sulollari, Sardar Ali, Saroj Sapkota, Saulius Tautvaisas, Sayambhu Sen, Sayantani Banerjee, Sebastien Diarra, SenthilNathan. M, Sewoong Lee, Shaan Shah, Shankar Venkitachalam, Sharifa Djurabaeva, Sharon Ibejih, Shivanya Shomir Dutta, Siddhant Gupta, Silvia Paniagua Suárez, Sina Ahmadi, Sivasuthan Sukumar, Siyuan Song, Snegha A., Sokratis Sofianopoulos, Sona Elza Simon, Sonja Benčina, Sophie Gvasalia, Sphurti Kirit More, Spyros Dragazis, Stephan P. Kaufhold, Suba. S, Sultan AlRashed, Surangika Ranathunga, Taiga Someya, Taja Kuzman Pungeršek, Tal Haklay, Tasi'u Jibril, Tatsuya Aoyama, Tea Abashidze, Terenz Jomar Dela Cruz, Terra Blevins, Themistoklis Nikas, Theresa Dora Idoko, Thu Mai Do, Tilek Chubakov, Tommaso Gargiani, Uma Rathore, Uni Johannesen, Uwuma Doris Ugwu, Vallerie Alexandra Putra, Vanya Bannihatti Kumar, Varsha Jeyarajalingam, Varvara Arzt, Vasudevan Nedumpozhimana, Viktoria Ondrejova, Viktoryia Horbik, Vishnu Vardhan Reddy Kummitha, Vuk Dinić, Walelign Tewabe Sewunetie, Winston Wu, Xiaojing Zhao, Yacouba Diarra, Yaniv Nikankin, Yash Mathur, Yixi Chen, Yiyuan Li, Yolanda Xavier, Yonatan Belinkov, Yusuf Ismail Abayomi, Zaid Alyafeai, Zhengyang Shan, Zhi Rui Tam, Zilu Tang, Zuzana Nadova, Baber Abbasi, Stella Biderman, David Stap, Duygu Ataman, Fabian Schmidt, Hila Gonen, Jiayi Wang, David Ifeoluwa Adelani,
• Abstract summary: We present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages.<n>The 116 language varieties in Global PIQA cover five continents, 14 language families, and 23 writing systems.<n>In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements.
• Score: 117.95352635059153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Do You Know About My Nation? Investigating Multilingual Language Models' Cultural Literacy Through Factual Knowledge [68.6805229085352]
  Most multilingual question-answering benchmarks do not factor in regional diversity in the information they capture.<n>XNationQA encompasses a total of 49,280 questions on the geography, culture, and history of nine countries, presented in seven languages.<n>We benchmark eight standard multilingual LLMs on XNationQA and evaluate them using two novel transference metrics.
  arXiv  Detail & Related papers  (2025-11-01T18:41:34Z)
• SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala [39.525952729268994]
  We introduce SinhalaMMLU, the first multiple-choice question answering benchmark designed specifically for Sinhala.<n>The dataset includes over 7,000 questions spanning secondary to collegiate education levels, aligned with the Sri Lankan national curriculum.<n>We evaluate 26 LLMs on SinhalaMMLU and observe that, while Claude 3.5 sonnet and GPT-4o achieve the highest average accuracies at 67% and 62% respectively, overall model performance remains limited.
  arXiv  Detail & Related papers  (2025-09-03T09:22:39Z)
• MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation [86.7047714187813]
  MMLU-ProX is a benchmark covering 29 languages, built on an English benchmark.<n>Each language version consists of 11,829 identical questions, enabling direct cross-linguistic comparisons.<n>To meet efficient evaluation needs, we provide a lite version containing 658 questions per language.
  arXiv  Detail & Related papers  (2025-03-13T15:59:20Z)
• All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages [73.93600813999306]
  ALM-bench is the largest and most comprehensive effort to date for evaluating LMMs across 100 languages.<n>It challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages.<n>The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions.
  arXiv  Detail & Related papers  (2024-11-25T15:44:42Z)
• MILU: A Multi-task Indic Language Understanding Benchmark [7.652738829153342]
  We introduce MILU, a comprehensive evaluation benchmark designed to assess Large Language Models in Indic languages.<n>With an India-centric design, MILU incorporates material from regional and state-level examinations, covering topics such as local history, arts, festivals, and laws, alongside standard subjects like science and mathematics.<n>Open multilingual models outperform language-specific fine-tuned models, which perform only slightly better than random baselines.
  arXiv  Detail & Related papers  (2024-11-04T19:17:17Z)
• GlobalBench: A Benchmark for Global Progress in Natural Language Processing [114.24519009839142]
  GlobalBench aims to track progress on all NLP datasets in all languages. Tracks estimated per-speaker utility and equity of technology across all languages. Currently, GlobalBench covers 966 datasets in 190 languages, and has 1,128 system submissions spanning 62 languages.
  arXiv  Detail & Related papers  (2023-05-24T04:36:32Z)
• Polyglot or Not? Measuring Multilingual Encyclopedic Knowledge in Foundation Models [0.0]
  We assess the ability of foundation models to recall encyclopedic knowledge across a wide range of linguistic contexts. We produce a 20-language dataset that contains 303k factual associations paired with counterfactuals. We evaluate 5 models in a multilingual test, and benchmark a diverse set of 24 models in an English-only test.
  arXiv  Detail & Related papers  (2023-05-23T04:31:39Z)
• Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages [62.730361829175415]
  MIRACL is a multilingual dataset we have built for the WSDM 2023 Cup challenge. It focuses on ad hoc retrieval across 18 different languages. Our goal is to spur research that will improve retrieval across a continuum of languages.
  arXiv  Detail & Related papers  (2022-10-18T16:47:18Z)
• SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection [81.85463892070085]
  The SIGMORPHON 2020 task on morphological reinflection aims to investigate systems' ability to generalize across typologically distinct languages. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages.
  arXiv  Detail & Related papers  (2020-06-20T13:24:14Z)

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.