Non-English large language models (LLMs) yield much less accurate results than their English counterparts, primarily because they are trained on significantly smaller datasets. As generative AI usage increases in enterprises, CIOs find that models designed for different languages often perform poorly. This situation arises not from malicious intent but from the inherent lack of comprehensive training data, leading to lower accuracy and more frequent hallucinations in non-English LLMs. Companies must address this challenge or risk suboptimal performance for non-English-speaking users.
Because they're trained on significantly smaller datasets, non-English LLMs produce far less accurate results than English-language models - a new headache for global company leaders.
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