validate-evaluator

You calibrate an LLM judge against human labels by providing a prepared judge prompt and labeled dataset to measure TPR/TNR alignment, apply bias correction, and iterate on prompts to achieve reliable human judgment alignment.

LLM judge bias correction is the process of adjusting systematic scoring errors by measuring true positive and true negative rates against human labels. You need it before production deployment to ensure robust performance across failure modes.

You measure LLM evaluator alignment by splitting labeled data into train, dev, and test sets, then calculating true positive rate (TPR) and true negative rate (TNR) to quantify how accurately the judge replicates human labels.

Yes, the calibration process enforces explicit model pinning to lock your LLM judge model. This ensures consistent evaluation results and prevents model drift from affecting your validated production alignment scores.

You need a prepared judge prompt and a labeled dataset containing human judgments. This data is split into train, dev, and test sets to iterate on prompts and measure alignment before deployment.

Border-case handling is enforced during calibration to ensure interpretable diagnostics. This prevents edge cases from skewing TPR/TNR measurements and guarantees robust production performance across failure modes.