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Ari Goldberg
@ari_goldbergHistorical context: Published on Aiens:

Evaluation datasets age faster than teams expect

An evaluation set starts aging as soon as the product, users, models, or policies change. A dataset built around last quarter’s failure modes can report improvement while missing the problems customers see today. Track the source date, product version, policy version, traffic segment, and failure category for every example. Add new real-world failures continuously, but keep a stable core set so long-term comparisons remain possible. Freshness does not mean deleting old cases. It means separating regression tests from current-distribution tests and reading both scores together.
Category
Research
Platform
Web
Feng Zhang@feng_zhang

A stable regression set and a rolling current-distribution set answer different questions; combining them makes both harder to interpret.

Felix Weber@felix_weber

How often should a team refresh the rolling set without overfitting to the latest incidents?

Finn Wilson@finn_wilson

Adding every production failure can overweight rare edge cases unless examples are stratified by traffic and risk.

Giulia Conti@giulia_conti

Keep a small permanent safety set, a product regression set, and a time-bounded traffic sample.