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    <abstract>Physical commonsense reasoning represents a fundamental capability of human intelligence, enabling individuals to understand their environment, predict future events, and navigate physical spaces. Recent years have witnessed growing interest in reasoning tasks within Natural Language Processing (NLP). However, no prior research has examined the performance of Large Language Models (LLMs) on non-question-answering (non-QA) physical commonsense reasoning tasks in low-resource languages such as Basque. Taking the Italian GITA as a starting point, this paper addresses this gap by presenting BasPhyCo, the first non-QA physical commonsense reasoning dataset for Basque, available in both standard and dialectal variants. We evaluate model performance across three hierarchical levels of commonsense understanding: (1) distinguishing between plausible and implausible narratives (accuracy), (2) identifying the conflicting element that renders a narrative implausible (consistency), and (3) determining the specific physical state that creates the implausibility (verifiability). These tasks were assessed using multiple multilingual LLMs as well as models pretrained specifically for Italian and Basque. Results indicate that, in terms of verifiability, LLMs exhibit limited physical commonsense capabilities in low-resource languages such as Basque, especially when processing dialectal variants.</abstract>
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%0 Conference Proceedings
%T Physical Commonsense Reasoning for Lower-Resourced Languages and Dialects: A Study on Basque
%A Bengoetxea, Jaione
%A Gonzalez-Dios, Itziar
%A Agerri, Rodrigo
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F bengoetxea-etal-2026-physical
%X Physical commonsense reasoning represents a fundamental capability of human intelligence, enabling individuals to understand their environment, predict future events, and navigate physical spaces. Recent years have witnessed growing interest in reasoning tasks within Natural Language Processing (NLP). However, no prior research has examined the performance of Large Language Models (LLMs) on non-question-answering (non-QA) physical commonsense reasoning tasks in low-resource languages such as Basque. Taking the Italian GITA as a starting point, this paper addresses this gap by presenting BasPhyCo, the first non-QA physical commonsense reasoning dataset for Basque, available in both standard and dialectal variants. We evaluate model performance across three hierarchical levels of commonsense understanding: (1) distinguishing between plausible and implausible narratives (accuracy), (2) identifying the conflicting element that renders a narrative implausible (consistency), and (3) determining the specific physical state that creates the implausibility (verifiability). These tasks were assessed using multiple multilingual LLMs as well as models pretrained specifically for Italian and Basque. Results indicate that, in terms of verifiability, LLMs exhibit limited physical commonsense capabilities in low-resource languages such as Basque, especially when processing dialectal variants.
%R 10.63317/3p6tfvjmet58
%U https://aclanthology.org/2026.lrec-1.264/
%U https://doi.org/10.63317/3p6tfvjmet58
%P 3343-3354

Markdown (Informal)

[Physical Commonsense Reasoning for Lower-Resourced Languages and Dialects: A Study on Basque](https://aclanthology.org/2026.lrec-1.264/) (Bengoetxea et al., LREC 2026)

• Physical Commonsense Reasoning for Lower-Resourced Languages and Dialects: A Study on Basque (Bengoetxea et al., LREC 2026)

ACL

• Jaione Bengoetxea, Itziar Gonzalez-Dios, and Rodrigo Agerri. 2026. Physical Commonsense Reasoning for Lower-Resourced Languages and Dialects: A Study on Basque. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 3343–3354, Palma de Mallorca, Spain. ELRA Language Resource Association.