A Mechanistic Account of Attention Sinks in GPT-2: One Circuit, Broader Implications for Mitigation

TL;DR

This paper investigates the causes of attention sinks in GPT-2 models, identifying key components responsible and exploring mitigation strategies across different architectures.

Contribution

It provides a mechanistic analysis of attention sinks, revealing multiple circuits that cause them and informing potential mitigation approaches.

Findings

Attention sink arises from interaction of query bias, MLP transformation, and key structure.

Each component causing sinks is individually dispensable in architecture.

Findings validated across natural language, math, and code inputs.

Abstract

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.