Modern day Language Models see extensive use in text classification, yet this comes at significant computational cost. Compute-effective classification models are needed for low-resource environments, most notably on edge devices. We introduce Adaptable Embeddings Networks (AEN), a novel dual-encoder architecture using Kernel Density Estimation (KDE). This architecture allows for runtime adaptation of classification criteria without retraining and is non-autoregressive. Through thorough synthetic data experimentation, we demonstrate our model outputs comparable and in certain cases superior results to that of autoregressive models an order of magnitude larger than AEN's size. The architecture's ability to preprocess and cache condition embeddings makes it ideal for edge computing applications and real-time monitoring systems.

本研究解决了现代语言模型在文本分类中由于计算成本高而导致的低资源环境应用的挑战。提出的适应性嵌入网络（AEN）是一种新颖的双编码器架构，利用核密度估计（KDE）实现分类标准的实时适应，且无需重训练。实验结果表明，该模型在某些情况下可以在数量级上输出与更大自回归模型相媲美或更优的结果，展示了在边缘计算和实时监控系统中的潜在应用价值。