Benchmark evaluation

How it works

A benchmark is a frozen set of inputs with graded answers — knowledge QA, grade-school arithmetic, domain-specific tasks. Running it turns a fuzzy 'is it good?' into a tracked accuracy you can diff across versions and providers. The real payoff is longitudinal: the same benchmark re-run on each release is an early-warning system for reasoning regression and model decay, exactly the kind of drift a single snapshot would miss.

When to use it

Tracking accuracy over time; comparing candidate models; gating releases on a quality bar.

Limitations

Only measures what the benchmark covers, saturates as models train toward it, and is inflated by contamination when the test set leaks into training. A high score is necessary, not sufficient — and a static benchmark ages as the field moves.

Method yield

Findings

1

Versions spanned

6

Yield score

3

Severity-weighted across the published findings below. Why we measure this →

Findings it surfaces (1)

Documented failures this method catches — the evidence it works.

• Errors in multi-digit arithmeticMedium

  Without tool use, models make systematic errors on multi-digit multiplication and long arithmetic.

  How it found it: A multiplication benchmark turns the failure into a tracked accuracy that worsens predictably with operand length.

  Reasoning

References & further reading

• Holistic Evaluation of Language Models

  Percy Liang, Rishi Bommasani, Tony Lee, et al. · arXiv:2211.09110 · November 16, 2022

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