Why MMLU, GSM8K, and HumanEval Are Broken (and What Should Replace Them)
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A model scores 90% on MMLU. It scores 95% on GSM8K. It scores 98% on HumanEval. The press release says: "State-of-the-art. Approaching AGI." You try the model yourself. It's good. It's not that good. It makes mistakes on simple tasks. It struggles with reasoning. It fails on tasks that are not in the training data. The benchmarks are lying. They are broken. This is the problem with standard benchmarks. They are saturated. They are leaky. They reward memorization. They do not measure true…
1Key Takeaways
- The press release says: "State-of-the-art.
- Approaching AGI." You try the model yourself.
- It fails on tasks that are not in the training data.
- This is the problem with standard benchmarks.
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that the press release says: "State-of-the-art.
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