Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation

A recent survey on Attention Sink (AS) in Transformers highlights a critical issue where excessive focus is placed on a limited number of uninformative tokens, complicating model interpretability and affecting training dynamics. This survey aims to consolidate existing research on AS and provide a structured framework for future advancements in the field.

The findings underscore the importance of addressing AS to improve the performance and reliability of Transformer models, which are foundational in various AI applications. By systematically analyzing AS, researchers can enhance model interpretability and mitigate issues such as hallucinations.

This development reflects a broader trend in AI research emphasizing the need for improved training dynamics and interpretability in models. As the field evolves, understanding the mechanisms behind attention allocation in Transformers becomes crucial, especially in light of emerging methods aimed at enhancing model efficiency and safety in applications like image generation and data quantization.