arXiv:2606.07604v1 Announce Type: new Abstract: 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 demonstrate that contribution weights provide a more fait
The rapid advancement and widespread deployment of LLMs demand more robust interpretability methods to understand their internal workings and improve their reliability.
Improved understanding of LLM information flow can lead to more efficient model design, better debugging, and enhanced trust in AI systems, impacting their adoption in critical applications.
The proposed 'Contribution Weights' offer a more nuanced and accurate metric for interpreting token influence within self-attention transformers, moving beyond the limitations of simple attention weights.
- · AI Researchers
- · LLM Developers
- · AI Ethics & Safety
- · Software Developers
- · Developers reliant on simplistic interpretability measures
- · Companies with opaque LLM systems
More sophisticated tools for analyzing LLM behavior become available to the research community.
This improved interpretability accelerates the development of more robust, transparent, and controllable AI models.
Enhanced trust in AI systems could lead to broader and faster integration of LLMs into highly sensitive or critical industries.
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Read at arXiv cs.LG