SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

2026-05-04

AI Summary

This study systematically evaluates the generalization of commonsense knowledge in large language models across multilingual and multicultural contexts, with a particular focus on low-resource languages and underrepresented cultures. Building upon a human-curated extension of the BLEnD benchmark encompassing over 30 language–culture pairs, the evaluation features two tracks—short-answer and multiple-choice—and strictly enforces a zero-shot setting, prohibiting any training or fine-tuning on the benchmark data while allowing participation from any NLP system. As the first large-scale, purely evaluative benchmark for cross-cultural commonsense reasoning, the initiative attracted registrations from over 140 teams, with 62 submitting results. Analysis reveals that state-of-the-art approaches perform substantially worse on low-resource languages, highlighting critical challenges in cultural alignment and cross-cultural commonsense transfer.

Abstract

We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an extended version of our manually constructed BLEnD benchmark (Myung et al. 2024), covering more than 30 language-culture pairs, predominantly representing low-resource languages spoken across multiple continents. As the task is designed strictly for evaluation, participants were not permitted to use the data for training, fine-tuning, few-shot learning, or any other form of model modification. Our task includes two tracks: (a) Short-Answer Questions (SAQ) and (b) Multiple-Choice Questions (MCQ). Participants were required to predict labels and were allowed to submit any NLP system and adopt diverse modelling strategies, provided that the benchmark was used solely for evaluation. The task attracted more than 140 registered participants, and we received final submissions from 62 teams, along with 19 system description papers. We report the results and present an analysis of the best-performing systems and the most commonly adopted approaches. Furthermore, we discuss shared insights into open questions and challenges related to evaluation, misalignment, and methodological perspectives on model behaviour in low-resource languages and for under-represented cultures.

Problem

• language adaptability
• cultural diversity
• low-resource languages
• everyday knowledge
• model evaluation

Innovation

• cross-lingual evaluation
• low-resource languages
• cultural adaptation
• LLM generalization
• zero-shot benchmarking