  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. Research in Tibetan NLP is inherently scarce and limited. While
there is currently no existing large language model for Tibetan due to its
low-resource nature, that day will undoubtedly arrive. Therefore, research on
efficient fine-tuning for low-resource language models like Tibetan is highly
necessary. Our research can serve as a reference to fill this crucial gap.
Efficient fine-tuning strategies for pre-trained language models (PLMs) in
Tibetan have seen minimal exploration. We conducted three types of efficient
fine-tuning experiments on the publicly available TNCC-title dataset:
"prompt-tuning," "Adapter lightweight fine-tuning," and "prompt-tuning +
Adapter fine-tuning." The experimental results demonstrate significant
improvements using these methods, providing valuable insights for advancing
Tibetan language applications in the context of pre-trained models.
