English-language scores cannot stand in for the experience of people using Arabic, Hindi, Swahili, Turkish or other languages and dialects.
Define whose language you are testing
Name the script, region, register and use case instead of reporting one undifferentiated language label. Written standard Arabic and dialectal customer support may require separate testing. A task may mix English product names with local-language instructions, dates and numbers. The evaluation should represent that realistic mix rather than literal translations of a few English benchmark prompts.
Translation of a test is not test equivalence
An English question may become easier or harder after translation because of grammar, cultural context or implicit assumptions. A reference answer may not have a unique phrasing in another language. Adapt examples through native-language subject experts, document changes and check that required evidence remains comparable. Where parity cannot be demonstrated, report results as separate task evaluations rather than a global language ranking.
Assess meaning, safety and access
Measure factual correctness, refusal appropriateness, preservation of negation, numerals and names, and comprehension of a locally relevant request. Include right-to-left rendering, keyboard navigation, screen-reader labels and caption quality when interfaces are involved. Fluency alone can hide wrong dosage units or legal caveats. Have qualified bilingual reviewers inspect high-impact cases.
Report more than an average
Show sample size, scenario categories, answerability and false-refusal rates by language. Averages can hide a near-total failure on a smaller but important population. For small slices, make uncertainty visible instead of asserting that one language is definitively worse. Include cases that look similar to training data and novel cases created with appropriate privacy controls.
A concrete service example
A global travel service asks a model whether an entry requirement changed for a citizen of a given country. One language’s answer cites last year’s rule while another gives a current source. Evaluate source date and jurisdiction separately for every language; do not score the answers as equally good merely because both are polite and grammatical.
Benchmark limitations
MLCommons describes AILuminate v1.0 as English-only and single-turn. That is an explicit boundary, not a failing of the project. Stanford HELM provides scenario-specific evaluation structures that can inform locally appropriate tests. No benchmark result should be stretched beyond its stated coverage.
Decision rule
Launch language support only when it meets an agreed task-specific quality gate, with human escalation when it does not. Repeat tests after product updates and after the content sources change. Publish missing-language coverage as unknown rather than silently presenting global availability.
Sources & Further Reading
- Holistic Evaluation of Language Models — Stanford CRFM.
- AILuminate benchmarks and methodology — MLCommons.
- NIST AI Risk Management Framework 1.0 — NIST.
Primary Scope and applicability vary by task and jurisdiction. This article is educational guidance, not legal, medical or financial advice.