AI Language Mixing Is Worse in Rare Languages

When AI Picks the Wrong Language

A LangOps architect at Chainels recently ran a controlled experiment that every localization buyer should read about. She was pre-translating roughly 24,000 English words into Norwegian Bokmål using Gemini inside Crowdin. The output looked fine. Grammatically coherent. Professional. Also wrong — littered with Swedish words substituted for Norwegian ones. Words like för instead of for, vecka instead of uke, varje instead of hvert. The write-up is detailed and worth your time.

The root cause is simple: large language models train on far more Swedish text than Norwegian or Danish text. When the model is uncertain, it gravitates toward the better-resourced neighbor. The structural plausibility masks the error. A non-native speaker reviewing at speed might miss every instance.

This is a real, documented failure mode in a well-resourced, well-studied language family with millions of speakers, professional linguist communities, and abundant reference corpora. Now consider what happens when the same dynamic plays out in languages with a fraction of that data.

The Training-Data Gap Is a Spectrum

Norwegian Bokmål has an estimated 4-5 million speakers. It has decades of digitized news, literature, and government documents. It still gets confused with Swedish by frontier AI models.

Chuukese has roughly 45,000 speakers. Pohnpeian has around 30,000. Neither language has a substantial presence in any major LLM training corpus. There is no meaningful volume of digitized Chuukese news archives, no large Pohnpeian web crawl, no standardized spell-checking dictionary that major AI providers have integrated into their pipelines.

Here is what that gap looks like in practical terms:

When an LLM encounters a Chuukese or Pohnpeian prompt, it does not subtly drift toward a closely related neighbor the way it does with Nordic languages. It may hallucinate vocabulary entirely, borrow heavily from English phonology, or produce output that reads as plausible Micronesian script while conveying medically inaccurate information. And unlike the Norwegian case, there is no built-in spellchecker to flag the errors. There is no large QA corpus to run strings against. The detection burden falls entirely on the human reviewer.

Why This Matters Most in Healthcare

The Chainels experiment involved a property management platform. The contaminated strings described reporting periods and turnover data. A mistranslated field label is a problem. It is not a patient safety problem.

Chuukese and Pohnpeian speakers in the United States are concentrated in communities with specific, documented healthcare access challenges. The Freely Associated States — Micronesia, Palau, Marshall Islands — have sent significant migrant populations to Hawaii, Guam, and the US Pacific Northwest. These communities rely on translated patient consent forms, discharge instructions, medication guides, and interpreter services delivered through Federally Qualified Health Centers and state Medicaid programs.

If AI pre-translation introduces an error into a Norwegian property management dashboard, a reviewer catches it. If AI pre-translation introduces an error into a Chuukese discharge instruction — and the reviewing linguist is under-resourced, fatigued, or simply not fluent enough to catch a subtle semantic drift — a patient may misunderstand their follow-up care.

The stakes are not equivalent. The failure mode is the same.

What Competent QA Actually Requires Here

The Chainels experiment solved the Norwegian problem with a smart combination of tools: Crowdin's spellchecker surfaced every contaminated string, Claude confirmed each flag was a genuine error, and a custom AI Pipeline prompt with an explicit false-friends list prevented recurrence on future runs.

That workflow depends on three things being available: a functioning spellchecker for the target language, a capable AI model that can audit output in that language, and a linguist who knows enough to design the intervention.

None of those dependencies are reliably available for Chuukese or Pohnpeian today. There is no production-ready Chuukese spellchecker embedded in any major TMS. Claude and GPT-4 can produce text that superficially resembles Chuukese. Neither model can reliably self-audit that output for accuracy.

This means the entire QA burden for AI-assisted Chuukese or Pohnpeian translation sits with the human reviewer. There is no automated backstop. The reviewer needs to be a fluent native or near-native speaker with subject matter awareness in the relevant domain — medical, legal, or educational — and enough experience to catch errors that look plausible at first read.

At TXLOC, we build every Chuukese and Pohnpeian project around that constraint. We use AI tools where they genuinely help — drafting, formatting, handling high-frequency English phrases that appear consistently across a document. We do not use raw AI output as a substitute for human translation in these language pairs, and we do not treat MTPE as a cost-saving default when the target population has no margin for error.

The Practical Takeaway

If you are buying translation for rare Pacific or Micronesian languages and your vendor mentions AI pre-translation as part of their workflow, ask one specific question: who is reviewing the output, what are their native language credentials, and what does their QA process look like when there is no automated detection tool to catch substitution errors?

The Nordic experiment is useful precisely because it makes the failure mode visible. In Chuukese and Pohnpeian translation, the same failure mode exists with fewer safety nets and higher downstream consequences. Visibility requires a qualified human — and that human needs to be in the workflow before the document reaches a patient, not after.

If you are working on healthcare or government translation for Micronesian communities and want to talk through how we structure QA for these language pairs, reach out to the TXLOC team.