When AI companies build large language models, they often assume that quantity equals quality. For English, this mostly works. For Arabic and Hebrew, it fails — and the failure shows up directly in your product.
The Arabic Problem: Dialects, Code-Switching, and Missing Context
Arabic is not one language. Modern Standard Arabic (Fusha) is what textbooks and formal documents use. But the 400+ million Arabic speakers actually speak in dialects — Egyptian, Gulf, Levantine, Moroccan — each with distinct vocabulary, grammar, and cultural references.
A model trained only on web text gets mostly formal Arabic and produces formal responses. Worse, code-switching — mixing Arabic with English or French mid-sentence — is how educated Arab users actually communicate online. If your training data does not include this pattern, your model will not handle it gracefully.
The Hebrew Problem: Under-Representation in Training Data
There are about 9 million Hebrew speakers in the world. The Hebrew text available on the internet is disproportionately formal, journalistic or governmental. Colloquial Israeli Hebrew, including modern slang and loan words, is severely under-represented in LLM training datasets.
What Native Speaker Data Actually Fixes
- Dialect coverage — Egyptian, Gulf, Levantine, Moroccan Arabic in natural speech
- Code-switching patterns — realistic Hebrew-English and Arabic-English mixing
- Cultural context — references, idioms and sensitivities a scraped corpus misses
- Intonation and prosody — for TTS and ASR models, native speakers provide the phonetic truth
- Annotation quality — a native speaker annotating sentiment or toxicity brings cultural understanding that a translated rubric cannot replicate
At AXOVATE, we collect and annotate Hebrew and Arabic data for AI companies with a pool of 100+ native speakers across dialects and registers.