PPI-corrected tests aim to fix false positives in LLM judge evaluations
IanArawjo · x · 2026-07-24
The post argues that LLM judges are unreliable without human calibration, and points to a PPI-style correction method as a way to reduce false positives.
- The author says the work is based on real LLM-judge data with human labels.
- A chart compares standard hypothesis tests against PPI-corrected variants.
- The key claim: when you do not correct for judge bias, standard tests can produce inflated false-positive rates; adding some human labels helps correct that.
- The author says evalstats is implementing these corrected statistical tests.
It’s essentially a methodology note for anyone using model-based evaluation judges.
Related event: Eliminating LLM Evaluation False Positives with PPI-Corrected Tests(5 posts)→
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