Summary
This research paper investigates two recurring issues in Transformer models: "massive activations," where a small number of tokens exhibit extreme values in specific channels, and "attention sinks," where certain tokens attract disproportionate attention. The study aims to clarify the functional roles and causal relationship between these often co-occurring phenomena, which are currently not well understood. Understanding these mechanisms could lead to more stable and efficient AI models.
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