We investigate a surprising limitation of LLMs: their inability to
consistently generate text in a user's desired language. We create the Language
Confusion Benchmark (LCB) to evaluate such failures, covering 15 typologically
diverse languages with existing and newly-created English and multilingual
prompts. We evaluate a range of LLMs on monolingual and cross-lingual
generation reflecting practical use cases, finding that Llama Instruct and
Mistral models exhibit high degrees of language confusion and even the
strongest models fail to consistently respond in the correct language. We
observe that base and English-centric instruct models are more prone to
language confusion, which is aggravated by complex prompts and high sampling
temperatures. We find that language confusion can be partially mitigated via
few-shot prompting, multilingual SFT and preference tuning. We release our
language confusion benchmark, which serves as a first layer of efficient,
scalable multilingual evaluation at
this https URL

LLMs have limitations in generating text in a user's desired language, and the Language Confusion Benchmark evaluates these failures, finding that Llama Instruct and Mistral models exhibit high degrees of language confusion, even the strongest models fail to consistently respond in the correct language.