Examples
Worked examples
- Is an instance
A procedurally generated benchmark of 100k multi-step arithmetic problems.
- Is an instance
An LLM-generated multi-choice probe of moral reasoning validated by human raters on a 5% sample.
Counter-examples
Looks similar, but isn't
- Not an instance
MMLU (human-curated benchmark).
- Not an instance
A real-world dataset of clinical notes.
Editorial commentary
A synthetic benchmark is a benchmark whose evaluation items are wholly or partially generated by another model or by a procedural method, rather than collected or curated from natural, human-produced sources. Synthetic benchmarks are attractive because they can scale to far more test items, far more cheaply, than human authorship allows, and can be deliberately constructed to probe a narrow capability precisely.
The validity risk this creates
A benchmark generated by a model risks being systematically biased toward that generator model’s own strengths and blind spots — a synthetic benchmark built using one model to write its questions can inadvertently favour models with similar training data or architecture to the generator, understating the performance gap for genuinely different systems. This is distinct from, but related to, data leakage: a synthetic benchmark generated from a corpus a later model was itself trained on can be answered from memorisation of the source material rather than from the reasoning capability the benchmark was meant to test. Any synthetic benchmark’s methodology should disclose the generator model or procedure used, and any validation performed to check the items are answerable, well-posed, and free of an obvious “tell” that reveals the intended answer.
How this differs from BIG-bench
BIG-bench‘s items are human-authored and community-peer-reviewed, not generated — it sits at the opposite end of the authorship spectrum from a synthetic benchmark, even though both are, in the broad sense, “constructed” rather than naturally occurring evaluation sets.
References
- See also: Synthetic data, Data leakage (training), Red-teaming.
Also known as
model-generated benchmark · synthetic eval
Machine-readable encodings
Use in your systems
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