Enhancing Transformers Through Conditioned Embedded Tokens
This addresses a fundamental optimization issue in transformers, which are widely used in machine learning, but the approach appears incremental as it builds on existing transformer frameworks.
The paper tackled the problem of inherent ill-conditioning in transformer attention blocks, which hampers gradient-based optimization and training efficiency, by introducing conditioned embedded tokens to improve conditioning, resulting in more stable and efficient training with consistent improvements across various transformer architectures in tasks like image classification and natural language processing.
Transformers have transformed modern machine learning, driving breakthroughs in computer vision, natural language processing, and robotics. At the core of their success lies the attention mechanism, which enables the modeling of global dependencies among input tokens. However, we reveal that the attention block in transformers suffers from inherent ill-conditioning, which hampers gradient-based optimization and leads to inefficient training. To address this, we develop a theoretical framework that establishes a direct relationship between the conditioning of the attention block and that of the embedded tokenized data. Building on this insight, we introduce conditioned embedded tokens, a method that systematically modifies the embedded tokens to improve the conditioning of the attention mechanism. Our analysis demonstrates that this approach significantly mitigates ill-conditioning, leading to more stable and efficient training. We validate our methodology across various transformer architectures, achieving consistent improvements in image classification, object detection, instance segmentation, and natural language processing, highlighting its broad applicability and effectiveness.