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Data for LLM training & fine-tuning
SFT datasets, reasoning traces, instruction data, multi-turn conversations, and domain knowledge — in Arabic, regional dialects, and multilingual pairs.
Language model teams working on Arabic or multilingual capability run into the same wall eventually: generic web-scraped Arabic text skews Modern Standard and formal-register, and most instruction datasets are written or translated by people who don't speak the target dialect natively. We build task-specific instruction and conversation data with contributors matched to the register and dialect the model actually needs to handle.
What this covers
- Supervised fine-tuning (SFT) instruction-response pairs
- Multi-turn conversational data, including code-switched and dialectal turns
- Reasoning traces reviewed for whether intermediate steps actually support the conclusion
- Domain-specific corpora (legal, medical, financial Arabic)
- Translation and localization pairs, reviewed for register accuracy not just literal correctness
Worked example. A model team needed 100,000 Arabic response-preference rankings to align a chat model's tone for Gulf users. We recruited Gulf-based reviewers, ran pairwise preference tasks against a written rubric, and delivered the rankings with per-item inter-annotator agreement scores.