ECE
Beyond Market Intelligence keeps ECE in one place: 2 stories so far. The section currently leads with “Small models, smarter labels: confidence scoring without the hype” and “A sharper alignment: Jev's confidence accuracy jumps 68%”. Small classification models that score confidence over a list of labels, rather than generating free text, deserve more attention. Jev's confidence accuracy improved 68% after learning from human-labeled examples, yet its hallucination-detection F1 barely budged. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every ECE story on Beyond Market Intelligence, newest first.
Small models, smarter labels: confidence scoring without the hype
Small classification models that score confidence over a list of labels, rather than generating free text, deserve more attention. Gokula Krishnan ran his own evals on TypeSafe AI's hosted Jev and the open-source Laya, and his findings are practical. Describing labels by what's in the input, not intent, boosted accuracy from 65% to 77% on AML detection. Calibration matters: Jev's ECE of 0.013 let a 90% confidence filter reach 97.6% accuracy while covering 82% of cases. Laya's untuned ECE of 0.486 didn't
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.