This article explores the adaptive relationship between Encoder Layers and Decoder Layers using the SOTA model Helsinki-NLP/opus-mt-de-en, which translates German to English. The specific method involves introducing a bias-free fully connected layer between the Encoder and Decoder, with different initializations of the layer's weights, and observing the outcomes of fine-tuning versus retraining. Four experiments were conducted in total. The results suggest that directly modifying the pre-trained model structure for fine-tuning yields suboptimal performance. However, upon observing the outcomes of the experiments with retraining, this structural adjustment shows significant potential.

翻译：本文利用当前最先进的模型Helsinki-NLP/opus-mt-de-en（用于德译英翻译），探讨了编码器层与解码器层之间的自适应关系。具体方法是在编码器与解码器之间引入一个无偏置的全连接层，通过设置该层权重的不同初始化值，观察微调与重新训练的效果差异。共进行了四项实验。结果表明，直接修改预训练模型结构进行微调的效果并不理想。然而，通过观察重新训练实验的结果，这种结构调整展现出显著的潜力。