PEFTT: Parameter-efficient Fine-tuning For Low-resource Tibetan Pre-trained Language Models

Abstract

In this era of large language models (LLMs), the traditional training of models has become increasingly unimaginable for regular users and institutions. The exploration of efficient fine-tuning for high-resource languages on these models is an undeniable trend that is gradually gaining popularity. However, there has been very little exploration for various low-resource languages, such as Tibetan.