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.

翻译：在大语言模型时代，传统模型训练模式对普通用户和机构而言已愈发难以实现。针对高资源语言的高效微调探索正逐渐成为不可逆转的趋势。然而，对于藏语等各类低资源语言的相关研究却极为匮乏。藏语自然语言处理的研究本就稀缺且受限。尽管由于资源稀缺性，目前尚不存在专门的藏语大语言模型，但这一天的到来毋庸置疑。因此，开展针对藏语等低资源语言模型的高效微调研究具有高度必要性。我们的研究可作为填补这一关键空白的参考。针对藏语预训练语言模型的高效微调策略此前鲜有探索。我们在公开的TNCC-title数据集上开展了三类高效微调实验："提示微调"、"适配器轻量级微调"及"提示微调+适配器联合微调"。实验结果表明，这些方法带来了显著性能提升，为推进预训练模型背景下藏语语言应用提供了宝贵启示。