China AI Benchmarks: Inspect Dataset Boundaries
Benchmark comparisons depend on what was tested, what was excluded and what the model had already seen. Ask for a dataset boundary note before ranking suppliers.
Direct answer. Ask for the benchmark's dataset boundary, evaluation settings and exclusions before comparing the score. A higher number on an unspecified test is not a sound basis for a procurement decision.
Identify the tested population
During a China AI benchmark presentation, request the evaluation name, version and sample definition. Ask whether the result comes from a public benchmark, the supplier's internal set or a customer-specific trial. These sources offer different levels of reproducibility and business relevance.
Do not assume access to the underlying data is permitted. A redacted method note can still state how samples were selected, who labeled them and which cases were excluded. If the host cannot disclose those conditions, label the score as a supplier-reported claim.
Write a dataset boundary note
| Field | Question |
|---|---|
| Provenance | Where did the test cases originate? |
| Separation | How were tuning and final evaluation kept apart? |
| Exclusions | Were refusals, timeouts or unreadable inputs counted? |
| Exposure | Could the model or developers have seen these cases before? |
| Relevance | Which parts resemble your intended operating workload? |
Repeated tuning against a test set can reduce the independence of its final score. Ask about a separately held evaluation rather than accusing a supplier of contamination without evidence. Record any unavailable information as uncertainty.
NIST AI RMF Core supports documenting experimental integrity and testing methods. It does not supply a universal benchmark ranking or certify the test shown during your visit.
Move from ranking to decision
A follow-up evaluation should use your permitted workload, a frozen method and a baseline meaningful to the business. Keep quality, cost and latency comparisons on the same configuration.
Use the company-visit checklist for evidence ownership. Plan the immersion with colleagues who can distinguish an interesting research score from an operating acceptance criterion.