MLCommons published material that safety assessments of base models cannot be automatically transferred to ready-made systems. The organization describes the problem of comparability of such assessments and reports work to improve it.
According to the synopsis, fine-tuning can unpredictably change the model's safety profile, and many existing tests are not sensitive enough to notice such changes.
For teams deploying complete systems, this means the need to test the deployed configuration itself, not merely rely on the base model metrics. The source does not disclose in the provided package a specific MLCommons methodology or timelines for its implementation.
editorial commentary
Why it matters
An expected consequence is that teams will more often evaluate specific deployments after fine-tuning, rather than deem the base metric sufficient. The next observable signal will be MLCommons publishing a description of the methodology for comparable tests. Substantial uncertainty is tied to the absence of details about methods and results in the original package.