Multilingual Encoder Knows more than You Realize: Shared Weights
  Pretraining for Extremely Low-Resource Languages
Authors: name: Zeli Su, name: Ziyin Zhang, name: Guixian Xu, name: Jianing Liu, name: XU Han, name: Ting Zhang, name: Yushuang Dong

While multilingual language models like XLM-R have advanced multilingualism
in NLP, they still perform poorly in extremely low-resource languages. This
situation is exacerbated by the fact that modern LLMs such as LLaMA and Qwen
support far fewer languages than XLM-R, making text generation models
non-existent for many languages in the world. To tackle this challenge, we
propose a novel framework for adapting multilingual encoders to text generation
in extremely low-resource languages. By reusing the weights between the encoder
and the decoder, our framework allows the model to leverage the learned
semantic space of the encoder, enabling efficient learning and effective
generalization in low-resource languages. Applying this framework to four
Chinese minority languages, we present XLM-SWCM, and demonstrate its superior
performance on various downstream tasks even when compared with much larger
models.