Bilingual RLHF annotation for LLM alignment

Bad preference data trains bad models

RLHF is only as good as the humans providing feedback. Generic crowdsourced annotators often default to surface-level preferences — longer responses, more confident tone — without evaluating actual correctness, reasoning quality, or factual grounding. The result: models that sound right more often than they are right.

Anchorver's approach is different. Our annotators are trained to evaluate substance, not style — reasoning quality, factual accuracy, and instruction-following — with founder-level review on edge cases. And because we annotate natively in both English and French, your model's alignment quality does not degrade the moment it switches languages.

RLHF data, done properly

A process built for LLM teams

• Rubric-aligned annotation — we work from your evaluation criteria, not generic preference heuristics.
• Bilingual by default — every annotator works natively in English or French, so quality does not drop across languages.
• Inter-annotator agreement tracked — Cohen's Kappa and Fleiss' Kappa scores reported on every delivery.
• Founder-level escalation — ambiguous or high-stakes cases are reviewed before delivery, not buried in the dataset.