← 📊 Evaluation & ML Foundations
Core
Benchmarks and Their Limits
Public benchmarks like MMLU, HumanEval and HELM give a single comparable number, which is why they fill leaderboards. They are also a weak proxy for whether a model works on your customer's task, because of train-test contamination, overfitting to the benchmark, narrow construct validity, and the gap between a generic test and a specific job. The credible move is to build a task-specific eval set, not to quote a leaderboard.
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