Gemini · United Arab Emirates · Hugging Face
Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance
Compiled by KHAO Editorial — aggregated from 1 source. See llms.txt for citation guidance.
★ Tier-1 Source
Arabic is a family of languages living under one name.
Key facts
- On dialect fidelity, Falcon-Emirati-7B scores 0.52 (partial credit) against 0.05 for ALLaM, 0.03 for gemma-3-27b-it, 0.02 for Jais-2-8B-Chat, and effectively 0.00 for Fanar-2-27B-Instruct
- So alongside Alyah, they ran a second evaluation: open-ended generation on the same 1,173 Alyah questions, scored by an LLM judge (Gemini 3.7 Flash) against five models, Falcon-Emirati-7B
- Fanar-2-27B-Instruct stands out for a second reason too: it abstains far more than any other model, declining to answer 26.2% of the time, versus under 5% for every other model in the comparison
- LLM-judged correctness on the 1,173 Alyah questions, open-ended generation, Gemini 3.7 as judge
Summary
In the UAE, day-to-day conversation, humor, negotiation, and storytelling happen in Emirati Arabic, a Gulf dialect with its own vocabulary, its own rhythm, and a culture wrapped tightly around it. The team didn't start from scratch. The family spans three scales (3B, 7B, and 34B parameters) with context windows up to 128K and 256K tokens, and it was already trained on a broad mix of MSA and dialectal Arabic (Gulf, Levantine, Egyptian, Maghrebi) alongside English and multilingual data. That gave them a strong starting point: a model that already understood Arabic broadly, handled long context well, and had some dialectal exposure baked in. The team built Falcon-Emirati-7B on the 7B variant specifically. Turning a general Arabic model into an Emirati-dialect specialist sounds like a smaller job than building the base model in the first place.