LLM Benchmarks: MMLU, HumanEval, HELM, GPQA, MATH
Benchmarks are standardized tests for models: a fixed question set plus a scoring rule. This topic walks through MMLU, HumanEval, MATH and GPQA — what they ask, the famous scores, where each one saturates or leaks — plus HELM's multi-metric idea and the documented failure modes (contamination, weak graders, Goodharted leaderboards) every engineer must know.
01.The Problem: "Is This Model Good?" Needs a Test
Two labs release models. One claims "state of the art." The other claims "best reasoning." Compared by what?
You cannot interview a model. You need a benchmark: a fixed set of tasks, a fixed scoring rule, and everyone runs the same exam.
A benchmark is a standardized test for models — questions + grader + leaderboard.
The idea is exactly like a college entrance exam:
- Questions should be unseen by the test-taker (else it memorizes answers → contamination).
- The grader should be strict (a lenient examiner inflates everyone — until nobody can be ranked → weak graders).
- Once every applicant scores 99%, the exam stops sorting students → saturation.
- Coaching schools learn to game the format → Goodharting.
Every failure mode above actually happened to LLM benchmarks, and this topic covers the receipts. The four names you will be asked about:
- MMLU — multiple-choice school tests across 57 subjects. "How much does it know?"
- HumanEval — 164 hand-written Python coding problems. "Can it write working code?"
- MATH — 12,500 competition math problems. "Can it do multi-step reasoning?"
- GPQA — 448 brutally hard questions written by PhDs. "Can it beat the experts?"
Plus HELM, which is not another exam but a rule: never rank by one number.
One framing to hold onto: choose the eval by the decision the number must support. "Is it safe to ship?" is a different exam than "is it good at my domain?" — which is a different exam than "is my pipeline better than yesterday's?"
Benchmark Taxonomy: Match the Eval to the Decision 🧭
Benchmark Taxonomy: Match the Eval to the Decision 🧭
Static capability suites saturate and leak; holistic frameworks measure many metrics at once; preference and task-execution evals track deployed quality. Choose by the decision the number must support.
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