How are people handling multilingual LLM evals in…
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How are people handling multilingual LLM evals in production?
One thing I've been recently thinking about at work is how easy it is for multilingual quality issues to slip through if your evals are mostly in English.
Let's say your user base is something like:
* 70% English
* 20% Spanish
* 10% Japanese
Maybe an odd split, but just for the sake of argument. Localization can already be a huge pain and if your evaluation suite is almost entirely English, you can end up with excellent overall scores while users in other languages have a noticeably worse experience.
Some questions I've been wondering about:
* Do you maintain separate eval datasets for each supported language?
* Do you translate the same benchmark into multiple languages, or write language-native test cases?
* Do you report metrics per language, or only an overall score?
* How do you decide which languages deserve dedicated eval coverage?
* Have you found certain tasks (tool calling, structured output, reasoning, RAG, etc.) degrade more than others across languages?
It feels like multilingual evaluation is much less discussed than model selection or prompt engineering, even though it's probably one of the biggest sources of hidden quality problems for products with international users.
Would love to hear what people are doing in production, especially anything that's worked well.
#dev #programming #technology
One thing I've been recently thinking about at work is how easy it is for multilingual quality issues to slip through if your evals are mostly in English.
Let's say your user base is something like:
* 70% English
* 20% Spanish
* 10% Japanese
Maybe an odd split, but just for the sake of argument. Localization can already be a huge pain and if your evaluation suite is almost entirely English, you can end up with excellent overall scores while users in other languages have a noticeably worse experience.
Some questions I've been wondering about:
* Do you maintain separate eval datasets for each supported language?
* Do you translate the same benchmark into multiple languages, or write language-native test cases?
* Do you report metrics per language, or only an overall score?
* How do you decide which languages deserve dedicated eval coverage?
* Have you found certain tasks (tool calling, structured output, reasoning, RAG, etc.) degrade more than others across languages?
It feels like multilingual evaluation is much less discussed than model selection or prompt engineering, even though it's probably one of the biggest sources of hidden quality problems for products with international users.
Would love to hear what people are doing in production, especially anything that's worked well.
#dev #programming #technology
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