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Core
Benchmarks and Their Limits
Public benchmarks like MMLU offer a shared yardstick, but they saturate, leak into training corpora, and stop tracking real ability once labs optimize for them. Contamination (test items in the training data) and Goodhart's law (a measure that becomes a target stops measuring) are why a high leaderboard score can mean nothing on your workload. AI, ML, and GenAI engineer interviews probe this to see whether you trust a number or build a private eval set on your own distribution.
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