How to test three languages: run Chinese, Traditional Chinese and English separately
Cross-lingual consistency research shows answers change with language, so multilingual markets must be asked and measured language by language.
Key numbers
- Finding one: multilingual models frequently answer semantically equivalent questions inconsistently, which is why cross-lingual consistency (CLC) is its own field; direct distribution alignment in post-training improved consistency across three model families and three benchmarks.
- Finding two: latent-space intervention substantially raised English-to-non-English factual consistency (Spearman gains of 0.16 for Arabic, 0.20 for Russian) — but the underlying language differences do not disappear on their own.
- Finding three: in a multilingual citation audit, non-English queries surfaced fewer citations and were routed to language-appropriate resources significantly less often.
- Finding four: engines differ sharply in cross-language stability and sensitivity to phrasing, so results in one language cannot be extrapolated to another.
Method and sample
Cross-reading of three studies: cross-lingual consistency method evaluation (arXiv:2609.04409), latent-space factual consistency intervention (arXiv:2608.28860) and a multi-platform multilingual citation audit (arXiv:2609.00319), plus comparative work on AI search versus Google across languages (arXiv:2509.08919).
What it means for brands
The same brand asked about in Chinese, Traditional Chinese and English can get different answers and different cited sources. Multilingual is not translation: it is three separate visibility surfaces that need separate prompts, separate mention and citation data, and a consistency check across the three.
How SourceSight handles it
SourceSight's operating rule: one project per market, which means one prompt language. Teams covering three languages create three projects grouped together, run the same question set in each, then compare whether the same fact is described consistently across all three.
Limits
These findings come from academic benchmarks and panel data rather than brand-level field measurement; how large the language gap is for a specific industry has to be established with your own project data.
References
Run it on your own project
What the same prompts return across 10 engines is only knowable by running them.
Related research
Same engine, different surface, different answer: test app, web and API separately
On the same platform and the same questions, app and web cite systematically different source sets — and the API shows something else again.
Methods · 2026-10-07How to design a retest you can trust: five moves so noise does not pass as progress
AI visibility is a distribution, not a point: fix the prompts, repeat the runs, add controls, paraphrase, validate by hand — or you cannot attribute the change.