Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at the input layer of the PLM, without modifying its parameters. This paper investigates the potential of SPT for cross-lingual transfer. Unlike previous studies on SPT for cross-lingual transfer that often fine-tune both the soft prompt and the model parameters, we adhere to the original intent of SPT by keeping the model parameters frozen and only training the soft prompt. This does not only reduce the computational cost and storage overhead of full-model fine-tuning, but we also demonstrate that this very parameter efficiency intrinsic to SPT can enhance cross-lingual transfer performance to linguistically distant languages. Moreover, we explore how different factors related to the prompt, such as the length or its reparameterization, affect cross-lingual transfer performance.

翻译：软提示调优（Soft Prompt Tuning, SPT）是一种参数高效的方法，通过在预训练语言模型（PLM）的输入层插入可学习的嵌入（即软提示），在不修改模型参数的情况下使其适应特定任务。本文研究了SPT在跨语言迁移中的潜力。与以往跨语言迁移研究中常同时微调软提示和模型参数的做法不同，我们遵循SPT的原始设计理念，保持模型参数固定，仅训练软提示。这不仅降低了全模型微调的计算成本与存储开销，更证明了SPT固有的参数高效性能有效提升向语言距离较远语言的跨语言迁移性能。此外，我们探讨了与提示相关的不同因素（如提示长度或其重参数化）如何影响跨语言迁移性能。