多语言编码器蕴含超乎想象的知识：面向极低资源语言的共享权重预训练

摘要

尽管如XLM-R等多语言模型在推动NLP领域多语言处理方面取得了进展，但在极度低资源语言上的表现仍不尽如人意。这一状况因现代大语言模型（如LLaMA和Qwen）支持的语言远少于XLM-R而进一步恶化，导致全球众多语言的文本生成模型几乎空白。为应对这一挑战，我们提出了一种创新框架，旨在将多语言编码器适配至极度低资源语言的文本生成任务中。通过复用编码器与解码器间的权重，该框架使模型能够利用编码器已习得的语义空间，从而在低资源语言中实现高效学习与有效泛化。我们将此框架应用于四种中国少数民族语言，推出了XLM-SWCM，并展示了其在多项下游任务上的卓越性能，即便与规模更大的模型相比也毫不逊色。

English

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.

多语言编码器蕴含超乎想象的知识：面向极低资源语言的共享权重预训练

Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages

摘要

Summary

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