What challenges arise with multilingual LLMs, and why does tokenization penalize some languages?
Multilingual models are uneven across languages, and most candidates cite only the obvious cause. The differentiator is the tokenizer: the same sentence can cost a non-English user several times as many tokens. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Multilingual models are uneven across languages, and most candidates cite only the obvious cause. The differentiator is the tokenizer: the same sentence can cost a non-English user several times as many tokens. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
