Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance

Falcon-Emirati-7B is a 7-billion-parameter language model developed by the Technology Innovation Institute to understand and generate Emirati Arabic, including its dialect-specific vocabulary, cultural references, idioms, poetry, and conversational tone. Built by adapting the Falcon-H1-Arabic model, which combines Mamba state-space components with Transformer attention, it was trained using curated native Emirati web content, Modern Standard Arabic material about Emirati culture, and synthetic dialect data constrained by Emirati glossaries and style rules. The developers tested different data mixtures and training methods, evaluating outputs through native-speaker review and the 1,173-question Alyah benchmark, which covers everyday expressions, etiquette, figurative language, heritage, and poetry. They report that the model achieved 84.83% accuracy on Alyah and 85.57% on UAE scenarios from the ArabCulture-Dialogue benchmark, outperforming several Arabic and multilingual comparison models, particularly in producing answers in Emirati rather than defaulting to Modern Standard Arabic. The article notes that dialect competence does not arise solely from model size and requires targeted data and evaluation, while acknowledging that the model may still make errors, reflect training-data biases, and struggle with rare or highly localized expressions.

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