Abstract
Large Language Models (LLMs) demonstrate exceptional zero-shot capabilities in various NLP tasks, significantly enhancing user experience and efficiency. However, this advantage is primarily limited to resource-rich languages. For the diverse array of low-resource languages, support remains inadequate, with the scarcity of training corpora considered the primary cause. We construct and open-source CUTE Chinese, Uyghur, Tibetan,English dataset, consisting of two 25GB sets of four-language corpora (one parallel and one non-parallel), obtained through machine translation. CUTE encompasses two resource-rich languages (Chinese and English) and two low-resource languages (Uyghur and Tibetan). Prior to constructing CUTE, human assessment validates that the machine translation quality between Chinese-Uyghur and Chinese-Tibetan approaches that of Chinese-English translation. CUTE represents the largest open-source corpus for Uyghur and Tibetan languages to date, and we demonstrate its effectiveness in enhancing LLMs' ability to process low-resource languages while investigating the role of corpus parallelism in cross-lingual transfer learning. The CUTE corpus and related models are made publicly available to the research community.
Abstract (translated)
大型语言模型（LLMs）在各种自然语言处理任务中表现出卓越的零样本学习能力，显著提升了用户体验和效率。然而，这一优势主要局限于资源丰富的语言。对于众多低资源语言而言，支持仍然不足，训练语料库的稀缺被认为是主要原因。 我们构建并开源了CUTE（Chinese, Uyghur, Tibetan, English）数据集，该数据集包含两组共计25GB的四语言语料库（一组为平行语料，另一组为非平行语料），这些语料通过机器翻译获得。CUTE涵盖了两种资源丰富的语言（中文和英文）以及两种低资源语言（维吾尔语和藏语）。在构建CUTE之前，经人工评估确认，从汉语到维吾尔语和从汉语到藏语的机器翻译质量接近于汉语到英语的翻译质量。 CUTE是迄今为止最大的公开可用的维吾尔语和藏语文本语料库。我们展示了该数据集能够增强LLMs处理低资源语言的能力，并研究了平行语料在跨语言迁移学习中的作用。CUTE语料及其相关模型已向研究界公开发布。
URL
https://arxiv.org/abs/2509.16914
