Parameter-shared pre-trained language models (PLMs) have emerged as a
successful approach in resource-constrained environments, enabling substantial
reductions in model storage and memory costs without significant performance
compromise. However, it is important to note that parameter sharing does not
alleviate computational burdens associated with inference, thus impeding its
practicality in situations characterized by limited stringent latency
requirements or computational resources. Building upon neural ordinary
differential equations (ODEs), we introduce a straightforward technique to
enhance the inference efficiency of parameter-shared PLMs. Additionally, we
propose a simple pre-training technique that leads to fully or partially shared
models capable of achieving even greater inference acceleration. The
experimental results demonstrate the effectiveness of our methods on both
autoregressive and autoencoding PLMs, providing novel insights into more
efficient utilization of parameter-shared models in resource-constrained
settings.

参数共享的预训练语言模型（PLMs）已成为资源受限环境中成功的方法，在不牺牲性能的情况下显著降低了模型存储和内存成本。本文利用神经常微分方程（ODEs）构建了一种简单的技术，提高了参数共享 PLMs 的推理效率，并提出了一种简单的预训练技术，实现了更大的推理加速。实验结果证明了我们方法在自回归和自编码 PLMs 上的有效性，并对在资源受限环境中更高效地利用参数共享模型提供了新的见解。