LLM Data: Expert Human Evaluation & RLHF for Urdu and South Asian Languages

Automatic metrics can't tell you whether a model's Urdu, Pashto or Sindhi is right. Native experts can. The provider runs human evaluation and preference data for multilingual models, using the same blind, adjudicated method behind our published Urdu Nastaliq benchmark. WHAT WE DELIVER - Rubric-based scoring of model outputs - Pairwise preference ranking (RLHF / DPO data) - Error annotation by type - Corrected reference answers written by experts - Red-teaming and safety review in local languages TASKS Transcription and OCR output, translation, summarisation, question answering and open-ended generation in Urdu, Persian, Sindhi, Pashto, Punjabi, Balochi, Saraiki and other South Asian languages. QUALITY CONTROLS - Experts are identity-verified and screened on a test set before live work. - Each item can be double-rated, with disagreements adjudicated. - Every rating traces to an expert ID and a guideline version. - Nothing is model-drafted. WHY IT MATTERS On our public Urdu sample, frontier models made a mistake in about 70% of lines, often without flagging any doubt. Urdu alone has about 246 million speakers (Ethnologue, 2025). ENGAGEMENT A scoping call sets your rubric, then a paid calibration batch, then production at agreed throughput. Delivery is JSONL or CSV via S3, Google Cloud Storage, SFTP or compressed file. Pricing is USD 150-250 per expert hour, or per item after scoping. Method sample: the provider.com/human-eval-dataset. Public sample on Hugging Face (evaluation-on