Large Language Models (LLMs) perform quite well with Literary Arabic, also known as Modern Standard Arabic, which is commonly used in news and official documents. However, the Arabic language is structured much more complexly: every region has its own dialect, and Emirati is one of them.
Why Arabic Dialects Pose a Challenge for LLMs
Why Dialects Are a Separate Challenge
The Emirati dialect differs from Standard Arabic not only in pronunciation but also has its own unique vocabulary, grammatical constructions, and cultural contexts. If a model was trained primarily on Classical Arabic or dialects from other countries, it might struggle to understand texts from the UAE.
Until now, there hasn't been a systematic way to verify how well models specifically understand the Emirati dialect. Testing a model on general Arabic tasks didn't provide a complete picture of its performance with a specific regional language.
How Researchers Developed the Alyah Benchmark
What the Researchers Did
A team from the Technology Innovation Institute in the UAE has developed a benchmark suite called «Alyah». It is a collection of test tasks designed to assess a model's ability to work with the Emirati dialect.
The suite includes several types of tasks:
- text comprehension and the ability to answer questions;
- verification of knowledge about the culture and history of the UAE;
- reasoning and logic tasks;
- working with real-life examples from everyday life.
All tasks are composed in the Emirati dialect and verified by native speakers. This is crucial because automatic translation or adaptation of texts from other dialects could distort the meaning.
Performance of Models on the Emirati Dialect Benchmark
Which Models Were Tested
The researchers tested several language models on the benchmark. Among them were both specialized Arabic models and large multilingual ones, including GPT-4 and other well-known systems.
The results revealed an interesting picture: Models that perform well with Standard Arabic do not always handle the Emirati dialect as confidently. Even large multilingual models trained on massive amounts of data sometimes faced difficulties related to region-specific expressions and cultural references.
At the same time, specialized Arabic models performed differently: some fared better because their training data contained more dialectal material, while others remained at the level of general multilingual solutions.
Importance of Dialect-Specific Benchmarks
Why Is This Needed
For developers, this is a tool that helps identify weak spots in models. If you are creating an app for users in the UAE – a chatbot, a voice assistant, or a support ticket system – it is important for you to know how well the model understands the specific language your users speak.
For researchers, it serves as a guideline. The presence of a standardized set of tasks makes it possible to compare models with each other and track progress. Without such benchmarks, it is difficult to understand whether a new version of a model has truly become better at working with a dialect or if just some general parameters have changed.
Future of Language Models and Regional Dialects
What's Next
«Alyah» represents a step towards making language models work better with regional language variants. The Emirati dialect is not the only one in need of such tools. There are dozens of dialects in the Arab world, and each has its own peculiarities.
The team has released the benchmark to the public, so any developer or researcher can use it to evaluate their models. This contributes to creating more inclusive technologies – ones that work not only with formal textbook language but also with the living speech of people.
It is unclear yet how quickly major companies will adapt their models based on the results of such tests. But the very fact that specialized benchmarks for regional dialects are appearing is already a signal that the industry is starting to pay attention to linguistic diversity beyond the main world languages.