SearcharxivSearch

arXiv · 2510.24081

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

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·Allan Hanbury·Alou Dembele·Alp Niksarli·Álvaro Arroyo·Amin Bajand·Amol Khanna·Ana Chkhaidze·Ana Carolina Condez·Anamaria-Roberta Hartl·Andiswa Mkhonto·Andrew Hoblitzell·Andrew Tran·Angelos Poulis·Anirban Majumder·Anjali Chaudhary·Anna Vacalopoulou·Annette Kuuipolani Kanahele Wong·Annika Simonsen·Anton Kovalev·Anupam Nayak·Ashvanth S·Ayodeji Lana·Ayu Purwarianti·Bashar Alhafni·Benedict Busole·Bernard Ghanem·Bharti Nathani·Biljana Stojanovska Đurić·Blessing Ogundipe·Bolaotan Agbonile·Bragi Bergsson·Bruce Torres Fischer·Burak Tutar·Burcu Çınar·Cade Kane·Can Udomcharoenchaikit·Chadi Helwe·Chaithra Reddy Nerella·Chen Cecilia Liu·Chiamaka Nwokolo·Christopher Homan·Clément Sampebgo·Cristina España-Bonet·Cynthia Amol·Daeyoep Lee·Dan Saattrup Smart·Dana Arad·Daniil Dzenhaliou·Dasol Choi·David Liu·David Semedo·David Anugraha·Deborah Popoola·Deividas Mataciunas·Delphine Nyaboke·Dennis Owusu·Dhyuthy Krishna Kumar·Diogo Tavares·Diogo Glória-Silva·Divyanshu Goyal·DongGeon Lee·E. Kelly Buchanan·Ebele Nwamaka Anajemba·Egonu Ngozi Grace·Elena Mickel·Elias Herranen·Eliza Acharya·Eman Nisar·Emile Anand·Emmanuel Habumuremyi·Emuobonuvie Maria Ajiboye·Eryawan Presma Yulianrifat·Esther Adenuga·Ewa Rudnicka·Faith Itiola

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 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.

Explore related subjects

Keep this discovery

BibTeXRIS

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, Allan Hanbury, Alou Dembele, Alp Niksarli, Álvaro Arroyo, Amin Bajand, Amol Khanna, Ana Chkhaidze, Ana Carolina Condez, Anamaria-Roberta Hartl, Andiswa Mkhonto, Andrew Hoblitzell, Andrew Tran, Angelos Poulis, Anirban Majumder, Anjali Chaudhary, Anna Vacalopoulou, Annette Kuuipolani Kanahele Wong, Annika Simonsen, Anton Kovalev, Anupam Nayak, Ashvanth S, Ayodeji Lana, Ayu Purwarianti, Bashar Alhafni, Benedict Busole, Bernard Ghanem, Bharti Nathani, Biljana Stojanovska Đurić, Blessing Ogundipe, Bolaotan Agbonile, Bragi Bergsson, Bruce Torres Fischer, Burak Tutar, Burcu Çınar, Cade Kane, Can Udomcharoenchaikit, Chadi Helwe, Chaithra Reddy Nerella, Chen Cecilia Liu, Chiamaka Nwokolo, Christopher Homan, Clément Sampebgo, Cristina España-Bonet, Cynthia Amol, Daeyoep Lee, Dan Saattrup Smart, Dana Arad, Daniil Dzenhaliou, Dasol Choi, David Liu, David Semedo, David Anugraha, Deborah Popoola, Deividas Mataciunas, Delphine Nyaboke, Dennis Owusu, Dhyuthy Krishna Kumar, Diogo Tavares, Diogo Glória-Silva, Divyanshu Goyal, DongGeon Lee, E. Kelly Buchanan, Ebele Nwamaka Anajemba, Egonu Ngozi Grace, Elena Mickel, Elias Herranen, Eliza Acharya, Eman Nisar, Emile Anand, Emmanuel Habumuremyi, Emuobonuvie Maria Ajiboye, Eryawan Presma Yulianrifat, Esther Adenuga, Ewa Rudnicka, Faith Itiola. 2025-10-28. Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures. https://arxiv.org/abs/2510.24081

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System

Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) long feedback loops, where model training can take days, making serial iteration prohibitively slow and requiring parallel exploration across multiple research directions; and (2) system complexity, where large configurations, fragile infrastructure dependencies, and multi-day GPU jobs require robust and recoverable execution. We present Auto-RecSys, an autonomous research system for long-horizon experimentation on industry-scale recommendation models. Auto-RecSys addresses these challenges through three harness designs: (1) distributed asynchronous execution for running multiple experiments in parallel across servers, (2) centralized cross-server memory for persistent and recoverable execution across sessions and failures, and (3) cognitive-procedural separation, where natural-language skill files guide LLM reasoning while deterministic scripts enforce operational correctness. Auto-RecSys further employs a dual-loop self-evolving architecture: an Execution Evolution Loop in which model-specific playbooks accumulate operational knowledge by recording failed attempts and crystallizing successful pipelines, and an Idea Evolution Loop in which experimental outcomes inform subsequent ideation. Evaluated on recommendation models, Auto-RecSys significantly reduces the human time required per experiment cycle and improves execution reliability as its playbooks mature.

cs.CL

Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation

Graph captions should help readers understand graph structure, rather than simply translate adjacency matrices into long textual edge lists. A useful graph caption abstracts connectivity into recognizable motifs, such as hubs, paths, cycles, cliques, and bridges, because these motifs provide compact structural units that are easier to read, compare, and recover. In this paper, we study motif-oriented graph captioning as a bidirectional graph-text translation task, where captions must both preserve enough topology for graph recovery and express the graph through concise motif-level descriptions. We show that direct prompting of GPT-5.1 often produces graph-recoverable captions by enumerating node-to-node connections, but these captions are verbose and can contain inconsistent motif interpretations. To address this gap, we introduce Structurally Speaking, a lightweight structured prompting protocol that guides translation between explicit connectivity and motif-level abstraction. Experiments on a synthetic motif-based dataset show that structured prompting produces shorter and more motif-consistent captions while maintaining comparable graph recovery. These results suggest that explicit topology-to-motif reasoning guidance can make LLM-generated graph captions more interpretable without model fine-tuning.

cs.CL

Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking

Turn-taking is a fundamental mechanism that governs when interlocutors speak and listen. Although Spoken Dialogue Systems (SDS) exploit a range of linguistic, acoustic, and non-verbal cues, they produce ill-timed responses in unscripted interaction. A central challenge is anticipating Transition Relevance Places (TRPs), or opportunities, not obligations, for a listener to take the floor. Human listeners do not wait for turn endings; as an utterance unfolds, they use expectations about its developing meaning to anticipate TRPs and decide whether to take the floor. We examine whether these evolving expectations can be modeled through semantic uncertainty -- an LLM-derived measure of how strongly a turn so far constrains what may plausibly come next. To do so, we sample possible continuations of an ongoing turn and use changes in semantic dispersion to identify TRPs within turns. We evaluate this account on a dataset with TRP labels derived from real-time listener responses, rather than retrospective annotation. Our approach substantially outperforms prompt-based and fine-tuned text-only baselines, providing empirical support for the view that evolving semantic constraints inform perceived turn-taking opportunities in unscripted interaction.

cs.CL