Large language models often claim to support over a hundred languages due to their extensive pretraining data, but this fluency does not equate to official support or verified benchmark performance. Vendors like Meta (Llama 3.1), Cohere (Command R), Alibaba (Qwen), and OpenAI (GPT-4) typically list far fewer languages, usually between eight and thirty, as officially supported on their model cards. This official list represents a commitment to evaluated performance, distinguishing it from languages merely present in the training data or those with published benchmark results, which are often limited to a subset of languages. AI
IMPACT Clarifies the distinction between observed fluency and officially supported languages in LLMs, impacting how users evaluate model capabilities.
RANK_REASON Article analyzes and explains claims made by LLM vendors regarding language support, rather than announcing a new release or product.
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