English results are strong but Chinese tasks fail: how do you evaluate languages separately?
Evaluate languages and actual tasks separately rather than infer Chinese performance from English totals. Translation can alter meaning or difficulty, and cultural knowledge and output budgets affect results.
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Evaluate languages and actual tasks separately rather than infer Chinese performance from English totals. Translation can alter meaning or difficulty, and cultural knowledge and output budgets affect results. Preserve native tasks and verified parallel items and report language-level metrics instead of only a blended average.
Define native Chinese, English and mixed-language tasks
This applies to fluent English summaries but weak Chinese names or instructions and score changes after translation. Define business tasks for Chinese, English and mixed inputs and record sample counts. Task structures can legitimately differ and need separate acceptance. Harder Chinese questions do not by themselves prove a language-capability deficit.
Check difficulty differences between translated and native samples
Check translated conditions, units and tone and culturally specific knowledge. Inspect tokenization and output budgets so one language is not truncated earlier. Evaluators need target-language competence. An English-strong judge may misread natural Chinese, requiring evidence checks instead of assumed neutrality.
Record settings and independent scoring evidence by language
Keep human-verified parallel tasks and native target-language samples and record versions and effective settings by language. Match content and criteria where possible and disclose nonequivalence. Check names, numbers and language switching separately and allow natural linguistic variation rather than demand literal English sentence structures in Chinese.
Report per-language outcomes and typical failures
Report pass results, causes and variability by language and task before optional aggregates. Determine whether changes reflect translation repairs, truncation or judge preference. Success means diagnosing actual language-use differences, not identical averages. A few translated pairs cannot establish fairness across cultures and professional domains.
This article ran no multilingual tests or ranked models. Research identifies linguistic and cultural biases without universal business calibration values. Native samples require rights and quality checks. Language performance and cultural knowledge are related but distinct. Aggregates can summarize but cannot replace language-level critical results.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.