Judgment-based preference modeling across cultures with explicit handling of linguistic ambiguity and disagreement.
Continuous calibration of evaluators over time (not per batch) to reduce variance and improve comparability.
Detect drift, bias, and instability early across regions and modalities.
SFT performance gains depend on data quality, —and data quality depends on consistent human judgment.
LILT co-designs and operates SFT programs that produce training data with measurable reliability across languages, domains, and modalities.
Preference ranking and pairwise comparisons across languages and cultural contexts.Rubric-driven evaluations for instruction-following, helpfulness, and policy adherence.Longitudinal monitoring to keep preference signals stable as models and policies evolve.Crowdsourced RLHF pipelines often drift over time and vary across locales.Disagreement gets suppressed instead of measured—masking real model failure modes.Research-designed rubrics, gold sets, anchor items to stabilize judgments.Readiness scoring, agreement tracking, and dynamic task gating based on confidence and risk.Enterprise-grade delivery accountability and auditability.