Why LLMs Are Unreliable Language Detectors

LLMs can translate, explain grammar, and write fluently in dozens of languages — so it seems obvious they should be great at simply identifying what language a text is written in. And for long, clean text in common languages, they usually are. But things fall apart at the edges that show up constantly in real-world use: short snippets, closely related languages, rare languages, mixed-language text, messy input, and situations where you actually need the system to say "I'm not sure." The problem isn't that LLMs don't understand language. It's that being able to talk fluently about a task isn't the same as reliably performing that task the way a production system needs. Generating text isn't the same as classifying itA real classifier picks from a fixed list of languages by scoring each one and returning the highest score. An LLM works differently: it generates a response one word-piece at a time, predicting each piece based on everything before it. So when you ask "What language is…