As a novel approach to tuning pre-trained models, prompt tuning involves freezing the parameters in downstream tasks while inserting trainable embeddings into inputs in the first layer.However,previous methods have mainly focused on the initialization of prompt embeddings. The question of how to train and utilize prompt embeddings in a reasonable way has become aa limiting factor in the effectiveness of prompt tuning. To address this issue, we introduce the Global Prompt Cell (GPC), a portable control module for prompt tuning that selectively preserves prompt information across all encoder layers. Our experimental results demonstrate a 5.8% improvement on SuperGLUE datasets compared to vanilla prompt tuning.
翻译:作为微调预训练模型的一种新方法,Prompt微调涉及在下游任务中冻结参数,在第一层中插入可训练的嵌入。然而,以前的方法主要集中在Prompt嵌入的初始化上。如何合理地训练和利用Prompt嵌入已成为影响Prompt微调有效性的限制因素。为解决这个问题,我们引入了全局Prompt单元(GPC),这是一个用于Prompt微调的可移植控制模块,可以选择地保留所有编码器层中的Prompt信息。我们的实验结果表明,在SuperGLUE数据集上,与普通Prompt微调相比,我们可以提高5.8%。