tiiuae/alyah-emirati-benchmark

Alyah ⭐️: Emirati Dialect Benchmark for Arabic LLMs

📝 Blogpost | 🔧 Code on GitHub

Dataset Summary

Alyah (الياه in Emirati means North Star) is a manually curated evaluation benchmark for assessing the performance of Arabic large language models on the Emirati dialect. It is designed to measure not only linguistic competence, but also cultural awareness, pragmatic understanding, and sensitivity to social norms as expressed in everyday Emirati Arabic.

The benchmark consists of 1,173 multiple-choice questions, each with four candidate answers and exactly one correct option. Alyah targets scenarios where dialectal fluency and culturally grounded reasoning are required, going beyond Modern Standard Arabic (MSA).

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Motivation

Despite Arabic being one of the most widely spoken languages globally, most existing benchmarks focus on Modern Standard Arabic, which differs substantially from how Arabic is used in daily life. Dialects—particularly Gulf and Emirati Arabic—remain underrepresented in evaluation settings, leading to blind spots in real-world model performance.

Alyah was created to address this gap by providing a high-quality, dialect-focused benchmark grounded in authentic Emirati usage. The goal is to support the development and evaluation of models that better serve users in the UAE and contribute to more inclusive Arabic language technology.

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Dataset Composition

All samples in Alyah were collected manually from native Emirati speakers, ensuring linguistic authenticity and cultural accuracy. For each question, large language models were used to synthetically generate distractor choices, which were then reviewed for plausibility and semantic proximity.

To avoid positional bias, the index of the correct answer is randomly distributed across the four options.

The dataset spans a diverse set of categories:

Category	Number of Samples	Difficulty
Greetings & Daily Expressions	61	Easy
Religious & Social Sensitivity	78	Medium
Imagery & Figurative Meaning	121	Medium
Etiquette & Values	173	Medium
Poetry & Creative Expression	32	Difficult
Historical & Heritage Knowledge	89	Difficult
Language & Dialect	619	Difficult

This distribution emphasizes dialect-specific linguistic phenomena while maintaining strong coverage of culturally and socially grounded knowledge.

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Data Format

Each sample follows a simple multiple-choice structure with the following fields:

• query: Question text written in Emirati dialect or culturally grounded Arabic
• option_1: first candidate answer
• option_2: second candidate answer
• option_3: third candidate answer
• option_4: fourth candidate answer
• correct_answer: Index of the correct answer (1–4)
• category: One of the predefined semantic or cultural categories

The dataset is suitable for zero-shot and few-shot evaluation of both base and instruction-tuned language models.

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Intended Use

Alyah is intended strictly as an evaluation benchmark.

It can be used for:

• Comparing Arabic-native, multilingual, and regionally adapted models
• Analyzing the effect of instruction tuning on dialectal understanding
• Category-level analysis of strengths and weaknesses in Emirati Arabic

It is not intended for training language models.

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Reference Evaluation

As a reference, we evaluated 53 models on Alyah, including 22 base models and 31 instruction-tuned models, spanning Arabic-native, multilingual, and adapted systems. Accuracy on the multiple-choice task is used as the primary metric.

Base Models (Reference Results)

Model	Accuracy
google/gemma-3-27b-pt	74.68
tiiuae/Falcon-H1-34B-Base	73.66
FreedomIntelligence/AceGPT-v2-32B	67.35
google/gemma-3-4b-pt	63.17
QCRI/Fanar-1-9B	62.75
tiiuae/Falcon-H1-7B-Base	60.78
meta-llama/Llama-3.1-8B	58.23
Qwen/Qwen3-14B-Base	57.29
inceptionai/jais-adapted-13b	56.01
Qwen/Qwen2.5-32B	53.03
FreedomIntelligence/AceGPT-13B	50.81
Qwen/Qwen2.5-72B	47.91
Qwen/Qwen2.5-14B	46.8
google/gemma-2-2b	41.86
tiiuae/Falcon3-7B-Base	41.43
Qwen/Qwen3-8B-Base	40.75
tiiuae/Falcon-H1-3B-Base	40.41
Qwen/Qwen2.5-7B	36.57
Qwen/Qwen2.5-3B	35.29
meta-llama/Llama-3.2-3B	35.12
inceptionai/jais-adapted-7b	33.5
Qwen/Qwen3-4B-Base	27.45

Instruction-Tuned Models (Reference Results)

Model	Accuracy
falcon-h1-arabic-7b-instruct	82.18
humain-ai/ALLaM-7B-Instruct-preview	77.24
google/gemma-3-27b-it	74.68
falcon-h1-arabic-3b-instruct	74.51
Qwen/Qwen2.5-72B-Instruct	74.6
CohereForAI/aya-expanse-32b	73.66
Navid-AI/Yehia-7B-preview	73.32
FreedomIntelligence/AceGPT-v2-32B-Chat	72.8
Qwen/Qwen2.5-32B-Instruct	71.61
tiiuae/Falcon-H1-34B-Instruct	71.1
meta-llama/Llama-3.3-70B-Instruct	69.74
QCRI/Fanar-1-9B-Instruct	69.22
tiiuae/Falcon-H1-7B-Instruct	65.13
CohereForAI/c4ai-command-r7b-arabic-02-2025	64.54
silma-ai/SILMA-9B-Instruct-v1.0	63.94
FreedomIntelligence/AceGPT-v2-8B-Chat	63.43
CohereLabs/aya-expanse-8b	61.21
yasserrmd/kallamni-2.6b-v1	61.13
yasserrmd/kallamni-4b-v1	60.7
microsoft/Phi-4-mini-instruct	58.57
tiiuae/Falcon-H1-3B-Instruct	57.12
silma-ai/SILMA-Kashif-2B-Instruct-v1.0	48.51
Qwen/Qwen2.5-7B-Instruct	45.44
google/gemma-3-4b-it	46.12
meta-llama/Llama-3.1-8B-Instruct	46.29
meta-llama/Llama-3.2-3B-Instruct	39.64
yasserrmd/kallamni-1.2b-v1	37.77
Qwen/Qwen3-4B	26.26
google/gemma-2-2b-it	26.00
Qwen/Qwen3-14B	26.00
Qwen/Qwen3-8B	25.66

These scores are provided for reference and reproducibility and should not be interpreted as universal rankings beyond the scope of Alyah.

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Evaluation Setup

All reference results reported in this benchmark were obtained using LightEval with Hugging Face Accelerate for multi-GPU execution. The evaluation treats Alyah as a multiple-choice task and reports accuracy as the primary metric. We submitted a PR to Lighteval to officially support Alyah but while waiting for the PR to be reviewed and accepted, we share a public fork to allow the usage of the benchmark. The following command illustrates how to reproduce the scores above:

git clone https://github.com/amztheorytii/lighteval_em.git
cd lighteval_em
pip install -e .[multilingual]
pip install language_data

accelerate launch --multi_gpu --num_processes=8 -m lighteval \
    accelerate \
    model_name=silma-ai/SILMA-Kashif-2B-Instruct-v1.0,batch_size=8 \
    'alyah' \
    --output-dir results \
    --load-tasks-multilingual \
    --save-details

This setup allows consistent evaluation across base and instruction-tuned models, supports distributed inference, and logs per-sample details for further analysis.

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Limitations

Alyah does not aim to exhaustively cover all Emirati dialectal variation. Language use varies across regions, generations, and social contexts within the UAE. While care was taken in reviewing synthetic distractors, subtle biases may still be present.

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Ethical Considerations

The dataset was curated with attention to cultural, social, and religious sensitivity. Content reflects commonly accepted Emirati contexts and norms. Alyah is intended for evaluation, not for training models on sensitive content.

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License

Falcon LLM Licence

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Citation

@misc{emirati_dialect_benchmark_2026,
title = {Alyah: An Emirati Dialect Benchmark for Evaluating Arabic Large Language Models},
author={Omar Alkaabi and Ahmed Alzubaidi and Hamza Alobeidli and Shaikha Alsuwaidi and Mohammed Alyafeai and Leen AlQadi and Basma El Amel Boussaha and Hakim Hacid},
year = {2026},
month = {january},
}