Contribution Weights: A Geometrical Analysis of Self-Attention Transformers

Analyzing attention weights has become a standard approach for interpreting the information flow of Large Language Models (LLMs). However, this approach has significant limitations as it neglects the geometric properties of the value vectors being aggregated. To address this gap, we introduce \emph{Contribution Weights}, a projection-based metric that quantifies a token's influence by accounting for it's attention weight, value magnitude, and directional alignment with the layer output. We demon

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ethics.ai (29 May 2026), “Contribution Weights: A Geometrical Analysis of Self-Attention Transformers,” evidence record 3444, https://ethics.ai/record/3444 (originally published by arXiv).

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