TII's Falcon-Emirati is an LLM tuned to the Emirati dialect and culture

The Technology Innovation Institute has announced Falcon-Emirati-7B, a language model adapted for Emirati Arabic and the cultural context behind it. In its Hugging Face post, TII reports 84.83% accuracy on Alyah, a benchmark of Emirati dialect and cultural knowledge. Readers can try the model on TII’s Falcon Chat platform.

The target is more specific than general Arabic fluency. Modern Standard Arabic is common in formal writing, while Emirati conversation uses local vocabulary, idioms and cultural references. TII built the model to understand those differences and answer in Emirati Arabic.

A 7B Arabic base with local training data

Falcon-Emirati uses the 7-billion-parameter version of Falcon-H1-Arabic. That family combines Mamba state space models with Transformer attention and already has training in Modern Standard Arabic and several Arabic dialects. TII says it chose 7B to balance dialect quality with training and inference costs.

The adaptation draws on Emirati websites and forums, Modern Standard Arabic material about Emirati culture, and synthetic dialect text. TII constrained the synthetic data with Emirati glossaries and grammar rules. The cultural material covers subjects such as heritage, customs and social norms, giving the model context alongside dialect vocabulary.

TII says it tested different data mixtures and training stages, using automatic scores and native-speaker reviews to judge progress. Native speakers assessed naturalness, tone and cultural fit.

Recognizing an answer and producing the dialect

Alyah contains 1,173 multiple-choice questions collected from native Emirati speakers. Its subjects include greetings, figurative language, heritage and poetry. TII’s chart puts Falcon-Emirati at 84.83%, ahead of Jais-2-8B-Chat at 78.09% and ALLaM-7B-Instruct-preview at 77.24%.

TII also asked models to generate answers to the same questions, then used an LLM judge to score correctness and dialect fidelity separately. Falcon-Emirati received a partial-credit dialect score of 0.52, compared with 0.05 for ALLaM and 0.03 for gemma-3-27b-it. TII attributes the difference to competing models often answering in Modern Standard Arabic.

A separate cultural test used 283 UAE scenarios from ArabCulture-Dialogue. Averaged across language varieties and location settings, Falcon-Emirati scored 85.57%, compared with ALLaM’s 83.39%. These are TII’s reported evaluations.

The model is available through Falcon Chat. TII warns that rare expressions and highly local references can still cause errors, and recommends use-case testing before sensitive or official use.

Benchmark context

The missing base-model comparison matters for adoption

TII excludes the Falcon-H1-Arabic family from its Alyah comparison because Falcon-Emirati builds on it. That leaves a useful question unanswered: how much does the dialect adaptation improve on Falcon-H1-Arabic-7B itself? The published chart supports a lead over the listed competitors, but a team already using the base model needs a direct comparison to judge the benefit of switching.

The generation and pairwise tests reuse Alyah’s 1,173 questions. They test different aspects of answers, but their agreement provides less evidence about new topics than evaluations on separate questions would. The 0.52 dialect score also needs its own interpretation. It is a judge’s partial-credit score, so readers should avoid reading it as the share of conversations that sound natural to native speakers.

For an Emirati chat application, compare the specialist and its 7B base on fresh prompts drawn from the intended service. Have native speakers assess factual correctness and dialect fit separately, including rare expressions that TII identifies as a weakness. That comparison would show whether the reported dialect advantage carries over to the conversations the application will handle.

Sources

Technology Innovation Institute: Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance. Announcement, evaluation results and lead chart. Image credit: Technology Innovation Institute.