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.

We investigate whether GPT-2 acquires Swedish grammatical structures in the same implicational order as for human second language (L2) learners, as predicted by Processability Theory (PT). We present SwePT – a minimal pair dataset targeting Swedish syntactic and morphological structures that are acquired by human L2 learners on four separate stages of language development – and evaluate the GPT-2 models on SwePT using an acceptability classification task throughout fine-tuning with different input orders in regards to the grammatical structures identified in the data. We find that the observed acquisition orders correlate across the fine-tuned models, while violating the implicational order sequence as hypothesized by PT. The observed relation between performance on the classification task and frequency distributions of the contrasting features in the minimal pairs suggests that the acquisition order can be explained by unigram and n-gram heuristics. While the adaptation of NLP methodologies into the PT framework requires further conceptual and methodological refinement, we do not find evidence for PT-like grammatical development in our experiments.

We present a new set of 96 Swedish multi-word expressions annotated with degree of (non-)compositionality. In contrast to most previous compositionality datasets we also consider syntactically complex constructions and publish a formal specification of each expression. This allows evaluation of computational models beyond word bigrams, which have so far been the norm. Finally, we use the annotations to evaluate a system for automatic compositionality estimation based on distributional semantics. Our analysis of the disagreements between human annotators and the distributional model reveal interesting questions related to the perception of compositionality, and should be informative to future work in the area.