arXiv:2606.06738v1 Announce Type: new Abstract: Building monolingual language models (LMs) for low-resource languages typically relies on adapting pretrained language models (PLMs) by finetuning the whole model on the target language. This approach is widely favored over training from scratch, as it enables effective knowledge transfer. Additionally, prior work has shown that using a language-specific tokenizer can enhance the adaptability. In this work, we hypothesize that full model tuning is often unnecessary and propose a more modular approach. Specifically, we replace the tokens, freeze t
The continuous drive for more efficient and adaptable AI models, particularly for under-resourced languages, makes this research timely within the broader language model development cycle.
This research suggests a more resource-efficient method for building effective language models for diverse languages, potentially broadening AI accessibility and reducing compute requirements for adaptation.
The proposed modular approach challenges the necessity of full model finetuning for low-resource languages, suggesting that targeted adaptation of specific components can be more efficient.
- · Developers targeting low-resource languages
- · Organizations with limited compute resources
- · NLP researchers focused on efficiency
- · Companies whose business models rely on extensive full-model finetuning
Reduced computational cost and time for developing AI models in many languages.
Increased diversity and accessibility of AI applications across a wider range of linguistic communities.
Potential for new business models specializing in modular AI adaptation services or highly localized AI solutions.
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