Alyah ⭐️: Toward Robust Evaluation of Emirati Dialect Capabilities in Arabic LLMs

Arabic is one of the most widely spoken languages in the world, with hundreds of millions of speakers across more than twenty countries. Despite this global reach, Arabic is not a monolithic language. Modern Standard Arabic coexists with a rich landscape of regional dialects that differ significantly in vocabulary, syntax, phonology, and cultural grounding. These dialects are the primary medium of daily communication, oral storytelling, poetry, and social interaction. However, most existing benchmarks for Arabic large language models focus almost exclusively on Modern Standard Arabic, leaving dialectal Arabic largely under-evaluated and under-represented.

This gap is particularly problematic as large language models increasingly interact with users in informal, culturally grounded, and conversational settings. A model that performs well on formal newswire text may still fail to understand a greeting, an idiomatic expression, or a short anecdote expressed in a local dialect. To address this limitation, our team introduces Alyah الياه (which means North Star ⭐️ in Emirati), an Emirati-centric benchmark designed to assess how well Arabic LLMs capture the linguistic, cultural, and pragmatic aspects of the Emirati dialect.

Benchmark Motivation and Scope

The Emirati dialect is deeply intertwined with local culture, heritage, and history. It appears in everyday greetings, oral poetry, proverbs, folk narratives, and expressions whose meanings cannot be inferred through literal translation alone. Our benchmark is intentionally designed to probe this depth. Rather than testing surface-level lexical knowledge, it challenges models on their ability to interpret culturally embedded meaning, pragmatic usage, and dialect-specific nuances.

Dataset Structure

Alyah spans a broad spectrum of linguistic and cultural phenomena in the Emirati dialect, ranging from everyday expressions to culturally sensitive and figurative language. The distribution across categories is summarized below.