What LILT enables
Verification workflows aligned to your domain constraints and policies.- Readiness scoring and dynamic gating based on evaluator performance and task risk.
- Comparable verification signals across languages and regions.
Why LILT for SFT
Evaluation signal governance
Gold sets and anchors are treated as measurement instruments, with longitudinal agreement tracking.
Drift detection is built in
Detect bias, instability, and rubric reinterpretation early—before signals degrade training.
Production-grade delivery
Enterprise-grade security, auditability, and accountability where trust breaks or holds.
Overview
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.
Challenges
- Verification criteria drift as tasks scale and evaluator populations change.
- “Equivalent” prompts and rubrics often behave differently across cultures and locales.
How LILT delivers
- Co-design verification rubrics, anchors, and calibration plans with your researchers.
- Continuous monitoring for variance, fatigue, misuse, and outlier behavior.
- In-pipeline reporting to connect human signals to model decisions.