Measuring LLM performance drift: observations and methodology from 31,352 repeated benchmark measurements [D]

Excerpt

One thing that has bothered me about LLM benchmarks for a while is that most of them are essentially snapshots. A model is evaluated, a score is published, and we tend to talk about that score as if it describes a relatively stable object. But with API-served models, the thing behind the model name can change over time: serving infrastructure changes, provider configurations change, versions change, and sometimes behaviour changes without an obvious public version transition. So we started appro