AI RESEARCH
Goldfish: Monolingual Language Models for 350 Languages
arXiv CS.CL
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ArXi:2408.10441v2 Announce Type: replace For many low-resource languages, the only available language models are large multilingual models trained on many languages simultaneously. Despite state-of-the-art performance on reasoning tasks, we find that these models still struggle with basic grammatical text generation in many languages. First, large multilingual models perform worse than bigrams for many languages (e.g. 24% of languages in XGLM 4.5B; 43% in BLOOM 7.1B) using FLORES perplexity as an evaluation metric.