Modular Approach to Adapting Language Models for Low-Resource Languages Shows Promise
Researchers propose a modular method for adapting pretrained language models to low-resource languages, replacing tokens and freezing embeddings rather than fine-tuning the entire model. The approach was tested on Scottish Gaelic, Irish, and Quechua — the last with only 8,500 training instances. Results show improved performance on natural language understanding tasks, suggesting full model fine-tuning may be unnecessary for low-resource adaptation.
A study accepted to the ACL 2026 Industry Track introduces a modular adaptation strategy for building monolingual language models in low-resource languages. Rather than fine-tuning an entire pretrained language model on the target language — the current dominant approach — the authors propose replacing tokens, freezing the corresponding embeddings, and tuning only the remaining model components. Experiments were conducted on Scottish Gaelic, Irish, and Quechua, with Quechua representing an extremely low-resource setting at just 8,500 training instances. Evaluation across natural language understanding tasks including mask filling, named entity recognition (NER), and part-of-speech (POS) tagging demonstrated that the modular approach outperforms full fine-tuning in low-resource scenarios. The paper also provides analysis of different training strategies, pretrained embedding choices, and model architectures. The findings suggest that language-specific tokenization combined with selective parameter tuning can be a more efficient and effective path to low-resource language model adaptation.
What's missing
The study does not report results on downstream generative tasks or cross-lingual transfer benchmarks, leaving open whether the modular approach generalizes beyond the three tested languages and the specific NLU tasks evaluated. It is also unclear how the method scales to languages with even fewer resources than Quechua or to larger model architectures such as decoder-only LLMs.
What different sources said
- arXiv cs.CLCenter
Modular Monolingual Adaptation using Pretrained Language Models
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