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

TL;DR

This paper introduces a shared evaluation task assessing multilingual and multicultural NLP systems' ability to handle low-resource languages using a new benchmark, with insights into system performance and challenges.

Contribution

It presents a novel multilingual, multicultural benchmark and an evaluation framework for assessing LLMs without training on the test data, highlighting diverse approaches and challenges.

Findings

Over 140 participants registered, with 62 teams submitting results.

Best systems demonstrated varied strategies for low-resource language evaluation.

Insights discussed include evaluation challenges and model behavior in under-represented cultures.

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.