Impact of Positional Encoding: Clean and Adversarial Rademacher Complexity for Transformers under In-Context Regression
NeutralArtificial Intelligence
- A recent study has analyzed the impact of positional encoding (PE) in Transformers, revealing that a trainable PE module increases the generalization gap in models during in-context regression. The research also highlights that models with PE are more vulnerable to adversarial attacks, as demonstrated by the derived adversarial Rademacher generalization bound and supported by simulation studies.
- This development is significant as it provides a clearer understanding of how PE affects the performance and robustness of Transformers, which are widely used in various AI applications. The findings suggest that while PE can enhance model capabilities, it may also introduce critical vulnerabilities that need to be addressed in future designs.
- The exploration of positional encoding in Transformers aligns with ongoing discussions in the AI community regarding model stability and efficiency. Various approaches, such as HybridNorm and alternative attention mechanisms, are being investigated to improve Transformer training and performance, indicating a broader trend towards refining model architectures to balance complexity and robustness.
— via World Pulse Now AI Editorial System
