Encoder Only Transformer

Encoder-only transformer models are neural networks designed for processing sequential data without a decoder component, focusing on efficient feature extraction and representation learning. Current research emphasizes improving their efficiency and interpretability, exploring techniques like localized attention mechanisms (e.g., Gaussian-based attention) and analyzing attention flows to understand their decision-making processes. These advancements aim to enhance the performance and resource efficiency of encoder-only transformers across diverse applications, from natural language processing and image captioning to network problem classification and logical reasoning tasks. The resulting improvements in speed, accuracy, and interpretability are driving significant progress in various fields.