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.
a free account unlocks the core curriculum tier · no card
Related concepts
Practise this in questions
ML System Design (Product)Design an evaluation framework for an ads-ranking system. →hard · FreeMachine Learning & Data ScienceYour model scored 95% in the pilot and 70% in production. What happened? →medium · FreeMachine Learning & Data ScienceExplain k-fold cross-validation, and when would you refuse to use it? →easy · FreeMachine Learning & Data ScienceA customer wants to forecast weekly demand. What's different about time-series ML, and how do you avoid embarrassing yourself? →medium · Free with sign-inMachine Learning & Data ScienceOffline metrics improved, but the online A/B shows no business lift. How do you explain the gap, and what do you do? →hard · PremiumMachine Learning & Data ScienceThe model aced every offline eval and the customer says it's useless. The labels look fine. What now? →hard · Premium
