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Sep 7 – Sep 13, 2026

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Domain-specific Pretraining Profile and Transformer Performance: Evidence from Modeling Digital Pragmatics in Arabic-English Code-switching

arXiv · · NLP Arabic AI

A study investigated the impact of domain-specific pretraining on Transformer models for modeling digital pragmatics in Arabic-English code-switched discourse. It compared MARBERT and XLM-R, using BERT as a baseline, on a dataset of 11695 X posts. MARBERT significantly outperformed XLM-R, achieving a Macro F1 of 0.85 on an independent test set compared to XLM-R's 0.52. The research concludes that Transformer performance in specialized pragmatic classification tasks relies more on a domain-specific pretraining profile than on multilingual coverage alone. Why it matters: This highlights the critical importance of tailored pretraining for achieving optimal performance in complex Arabic NLP tasks, particularly in code-switching contexts prevalent in the region.

YallaMorph: A Benchmark for Evaluating Arabic Morphological Generation in Large Language Models

arXiv · · NLP LLM

Researchers introduced YallaMorph, a new large-scale benchmark designed to evaluate Arabic morphological generation capabilities in large language models (LLMs). The benchmark covers over 600,000 entries across verbs, nouns, adjectives, their cliticized forms, and invalid configurations. Evaluations on multilingual and Arabic-oriented LLMs demonstrated that Arabic morphological generation remains challenging, particularly for cliticized, unseen, and morphologically rare forms. Why it matters: This benchmark provides a critical tool for advancing the development and accuracy of Arabic LLMs by directly addressing a core linguistic challenge for the language.

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