A Mechanistic Account of Attention Sinks in GPT-2: One Circuit, Broader Implications for Mitigation
For researchers studying transformer attention patterns, this work provides a mechanistic explanation of attention sinks and suggests that mitigation strategies must account for multiple possible causes.
The paper identifies a specific circuit causing attention sinks in GPT-2-style models, showing that the sink arises from the interaction of three components, each individually dispensable, suggesting sinks may arise through distinct circuits across architectures.
Transformers commonly exhibit an attention sink: disproportionately high attention to the first position. We study this behavior in GPT-2-style models with learned query biases and absolute positional embeddings. Combining structural analysis with causal interventions, validated across natural-language, mathematical, and code inputs, we find that the sink arises from the interaction among (i) a learned query bias, (ii) the first-layer MLP transformation of the positional encoding, and (iii) structure in the key projection. Crucially, each component we identify is individually dispensable: architectures omitting each of them robustly exhibit sinks. This indicates that attention sinks may arise through distinct circuits across architectures. These findings inform mitigation of sinks, and motivate broader investigation into why sinks emerge.