arXiv:2408.10441v3 Announce Type: replace Abstract: 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. Second, when we train small monolingual models with only 125M parame
The proliferation of large multilingual models has highlighted their limitations for low-resource languages, prompting researchers to explore more effective alternatives.
This development indicates a potential shift towards more efficient and effective AI solutions for a broader range of linguistic communities, reducing the dependency on large, often inefficient, multilingual models.
The focus for developing language models for low-resource languages may shift from scaling multilingual models to creating specialized monolingual models, leading to better performance and accessibility.
- · Speakers of low-resource languages
- · AI developers specializing in less common languages
- · Organizations requiring accurate AI for diverse linguistic markets
- · Providers of large, generic multilingual models
- · Research efforts solely focused on scaling multilingual models
Improved AI performance for specific, undertapped language markets.
Increased digital inclusion and economic participation for communities speaking low-resource languages.
Potential for new localized AI applications and services tailored to previously underserved linguistic groups, stimulating innovation outside dominant language spheres.
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