calibration error

A sharper alignment: Jev's confidence accuracy jumps 68%

Jev's confidence accuracy improved 68% after learning from human-labeled examples, yet its hallucination-detection F1 barely budged. That distinction matters. When a judge score determines automatic approvals or human escalations, poorly calibrated confidence quietly becomes a production problem. This is why building calibration into evaluation frameworks, like Typed Evals, matters more than treating raw confidence as trustworthy. For those exploring deeper measurement challenges, our piece on "Measure Embedding Relevance" offers a complementary look at tying benchmarks closer to real-world retrieval needs.