A representative corpus
Questions and scenarios should reflect the knowledge, reasoning and situations the benchmark claims to test.
Thomas Cole, The Consummation of Empire, 1836
Understand and interpret an artificial-intelligence benchmark
A benchmark measures a defined capability in a particular corpus, language and testing environment. It does not by itself show that a system is safe, compliant or useful in real work.
In 30 seconds
Essential points
- A score depends on the corpus, language, reference standard and execution conditions.
- Error profiles, stability and uncertainty matter as much as the average score.
- A general benchmark cannot establish competence in a specialised occupational-health system.
Definition
What is an AI benchmark?
A benchmark gives multiple models the same tasks under comparable conditions and compares their outputs with an explicit reference. It measures a bounded performance at a particular time.
A traceable reference standard
Expected answers, scoring rules and expert decisions should be documented and validated.
Reproducible execution
Model version, provider, settings, repetitions, cost, errors and missing answers should be reported.
Specialised evaluation
Why French occupational health requires direct testing
Strong performance in general medicine, law or French does not guarantee knowledge of the roles, procedures and reasoning specific to French occupational health.
Specific institutions and procedures
Employment law, occupational-health services, fitness decisions, job retention and relations with employers and worker representatives form a distinct system.
Medical, preventive and legal reasoning
Answers may require clinical knowledge, toxicology, ergonomics, prevention, work organisation, ethics and social law.
Plausible answers may still be professionally serious
Convincing wording can conceal confusion about legal roles, outdated rules or omission of collective prevention.
Critical appraisal
How to recognise a useful benchmark
Quality is not determined by item count alone. It depends on scope, comparability, uncertainty, error analysis and restraint in interpreting results.
01
Explicit scope
Readers can see what is being tested and which conclusions are not justified.
02
Comparable conditions
Models receive the same tasks and are scored under equivalent rules.
03
Measured uncertainty
Repeated runs and intervals help distinguish a stable signal from a chance difference.
04
Described error profiles
Omissions, over-inclusion, role confusion and instability complement the average score.
05
Cautious transfer to practice
Passing a benchmark does not automatically show that a tool is safe or useful in a real workflow.
Scientific article awaiting publication
A specialised benchmark for French occupational health
A scientific evaluation of large language models in this domain has been developed. Until the corresponding article is public, this page presents the rationale and methodological requirements without publishing detailed model rankings or results.