← Back to all news
arXiv Computation and Language By Abdu Sallouh, Nicholas Popovi\v{c}, Michael F\"{a}rber

Does Modern Standard Arabic (MSA) Dominate Arabic Dialects in LLMs? A Representation-Level Analysis

Read the original on arXiv Computation and Language →

The study investigates whether Modern Standard Arabic (MSA) dominates internal representations in large language models (LLMs) when generating Arabic, even if prompted with dialectal forms. By extending a language-dominance framework to 26 Arabic varieties, the authors find no evidence that MSA serves as a dominant internal representation; instead, dialect representations are densely overlapping, with mutual information dropping sharply compared to more distinct languages. The strongest separability effects vary across layers and architectures, indicating that generation bias toward MSA does not necessarily reflect internal representational dominance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Computation and Language
Sep 4

Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs

The paper investigates how Arabic dialects are represented in large language models and whether they can be steered at inference time. By analyzing neuron-level sparsity and vector steering, the authors find that only a small fraction of neurons encode dialect-specific features, while distributed activation directions are more effective for steering. Vector steering can induce dialectal output from both dialectal and MSA prompts, whereas neuron steering works only when the prompt is already dialectal.

By Kareem Elozeiri, Mervat Abassy, Omar Kallas, Fahim Dalvi, Preslav Nakov, Kentaro Inui, Nadir Durrani
llmsfine-tuningsafety
More like this →
arXiv AI
Sep 1

Beyond Fluency: A Rubric-Based Benchmark for Evaluating Saudi Dialect and Cultural Competence in Large Language Models

The paper introduces a rubric-based benchmark to evaluate Saudi Arabic dialect and cultural competence in large language models. It comprises 31 expert-authored prompts covering idiomatic, pragmatic, lexical, and culturally embedded aspects, each paired with an expert-established ground truth. Four state-of-the-art models were scored, revealing that none exceeded 55% accuracy and that ambiguous framing was the most common error type.

By Ghassan Al-Sumaidaee, Sajjad Abdoli, Ahmed Rashad, Maxim Legg
llmsbenchmarkssafety
More like this →
arXiv AI
Sep 24

Beyond Poetry: Can Large Language Models Generate Classical Arabic Maqamat?

arXiv:2609. 28245v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored.

By AbdulRahman A. Morsy (Department of Computer Science, School of Engineering and Applied Sciences, George Washington University, Washington DC, United States), Aya Zirikly (Department of Computer Science, School of Engineering and Applied Sciences, George Washington University, Washington DC, United States, Center for Speech and Language Processing, Whiting School of Engineering, Johns Hopkins University, Baltimore MD, United States)
llmssafety
More like this →