@inproceedings{ousidhoum-etal-2026-semeval,
title = "{S}em{E}val-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures",
author = "Ousidhoum, Nedjma and
Myung, Junho and
Perez Almendros, Carla and
Jin, Jiho and
Keleg, Amr and
Beloucif, Meriem and
Zhou, Yi and
Agerri, Rodrigo and
Araujo, Vladimir and
Baes, Naomi and
Barry, James and
Boisson, Joanne and
Chen, Nancy and
De Kock, Christine",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.455/",
pages = "3823--3837",
ISBN = "979-8-89176-414-9",
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. Our data and resources are available at https://github.com/BLEnD-SemEval2026/SemEval-2026-Task-7."
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<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. Our data and resources are available at https://github.com/BLEnD-SemEval2026/SemEval-2026-Task-7.</abstract>
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%0 Conference Proceedings
%T SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures
%A Ousidhoum, Nedjma
%A Myung, Junho
%A Perez Almendros, Carla
%A Jin, Jiho
%A Keleg, Amr
%A Beloucif, Meriem
%A Zhou, Yi
%A Agerri, Rodrigo
%A Araujo, Vladimir
%A Baes, Naomi
%A Barry, James
%A Boisson, Joanne
%A Chen, Nancy
%A De Kock, Christine
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F ousidhoum-etal-2026-semeval
%X 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. Our data and resources are available at https://github.com/BLEnD-SemEval2026/SemEval-2026-Task-7.
%U https://aclanthology.org/2026.semeval-1.455/
%P 3823-3837
Markdown (Informal)
[SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures](https://aclanthology.org/2026.semeval-1.455/) (Ousidhoum et al., SemEval 2026)
ACL
- Nedjma Ousidhoum, Junho Myung, Carla Perez Almendros, Jiho Jin, Amr Keleg, Meriem Beloucif, Yi Zhou, Rodrigo Agerri, Vladimir Araujo, Naomi Baes, James Barry, Joanne Boisson, Nancy Chen, and Christine De Kock. 2026. SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3823–3837, San Diego, California, USA. Association for Computational Linguistics.