Measuring LLM judge accuracy after model changes
A post recommends checking an LLM judge’s accuracy every time its own model changes, drawing a parallel with annual reviews of trading-surveillance systems.
TLDR
An LLM judge—a model used to evaluate AI outputs—should have its accuracy measured whenever its own model changes, one post argues. It cites chapter 5 of Evals for AI Engineers on estimating true success rates with imperfect judges. The comparison comes from MiFID II RTS 6, Article 13(6): the post quotes a requirement for investment firms engaged in algorithmic trading to review automated surveillance at least annually, including its ability to minimise false positive and false negative alerts.
Measuring LLM judge accuracy after model changes
A post recommends checking an LLM judge’s accuracy every time its own model changes, drawing a parallel with annual reviews of trading-surveillance systems.
