Search arXivSearch

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

Explore connections, maps & timelines

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. 2026-05-29. 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

Hierarchical attention interpretation: an interpretable speech-level transformer for bi-modal depression detection

Depression is a common mental disorder. Automatic depression detection tools using speech, enabled by machine learning, help early screening of depression. This paper addresses two limitations that may hinder the clinical implementations of such tools: noise resulting from segment-level labelling and a lack of model interpretability. We propose a bi-modal speech-level transformer to avoid segment-level labelling and introduce a hierarchical interpretation approach to provide both speech-level and sentence-level interpretations, based on gradient-weighted attention maps derived from all attention layers to track interactions between input features. We show that the proposed model outperforms a model that learns at a segment level ($p$=0.854, $r$=0.947, $F1$=0.897 compared to $p$=0.732, $r$=0.808, $F1$=0.768). For model interpretation, using one true positive sample, we show which sentences within a given speech are most relevant to depression detection; and which text tokens and Mel-spectrogram regions within these sentences are most relevant to depression detection. These interpretations allow clinicians to verify the validity of predictions made by depression detection tools, promoting their clinical implementations.

cs.CL

Cultural Alignment in Large Language Models Using Soft Prompt Tuning

Large Language Model (LLM) alignment is commonly achieved through supervised fine-tuning or reinforcement learning, both of which require labeled or preference data and update model weights. Without targeted cultural adaptation, however, deployed LLMs often exhibit culturally homogeneous behavior that fails to reflect diverse local values. Aligning models to cultural value frameworks such as Hofstede's Value Survey Module (VSM13) presents a distinct challenge: alignment signals are available only as aggregated survey-level scores computed after generating responses to an entire survey, providing no per-token gradient and requiring no preference data by construction. This makes standard gradient-based alignment methods ill-suited to the task. We propose a deployment-friendly approach that encodes cultural behavior in short, tunable soft prompts optimized with Differential Evolution (DE), while keeping model weights frozen and requiring no preference data. At inference, the system inserts the appropriate cultural-specific prompt to adapt model responses for different cultures. Experiments across four countries and four instruction-tuned models show that DE-optimized prompts generally reduce discrepancy with VSM13 reference profiles, improve rank agreement with the World Values Survey (WVS), an independent framework not seen during optimization, and are preferred in blinded pairwise evaluations using majority voting across three LLM judges.

cs.CL

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits

Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their extensive output vocabularies. The final linear projection layer, mapping hidden states to vocabulary-sized logits, often constitutes a substantial portion of the model's parameters and computational cost during inference. Existing methods like adaptive softmax or hierarchical softmax introduce structural complexities. In this paper, we propose VQ-Logits, a novel approach that leverages Vector Quantization (VQ) to drastically reduce the parameter count and computational load of the LLM output layer. VQ-Logits replaces the large V * dmodel output embedding matrix with a small, shared codebook of K embedding vectors (K << V ). Each token in the vocabulary is mapped to one of these K codebook vectors. The LLM predicts logits over this compact codebook, which are then efficiently "scattered" to the full vocabulary space using the learned or preassigned mapping. We demonstrate through extensive experiments on standard language modeling benchmarks (e.g., WikiText-103, C4) that VQ-Logits can achieve up to 99% parameter reduction in the output layer and 6x speedup in logit computation, with only a marginal 4% increase in perplexity compared to full softmax baselines. We further provide detailed ablation studies on codebook size, initialization, and learning strategies, showcasing the robustness and effectiveness of our approach.

cs.CL