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technologyreview.com·1h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Contribution Weights: A Geometrical Analysis of Self-Attention Transformers
TL;DR
Existing methods for interpreting Large Language Models (LLMs) using attention weights overlook the geometric properties of value vectors. Contribution Weights is a new metric that quantifies a token's influence by considering attention weight, value magnitude, and directional alignment.
✦ Why It Matters
Engineers can use Contribution Weights to improve model interpretability and optimize token influence in LLMs.
Key Takeaways
- Explore geometric properties of value vectors in LLMs.
- Implement Contribution Weights for better interpretability.
- Analyze token influence using attention weight and magnitude.
- Optimize model outputs based on directional alignment.
Full Summary
Interpreting the information flow in Large Language Models (LLMs) typically relies on analyzing attention weights, which represent how much focus a model gives to different tokens. However, this method fails to consider the geometric properties of the value vectors, which are crucial for understanding their contributions.
To fill this gap, Contribution Weights was developed as a projection-based metric that evaluates a token's influence by integrating its attention weight, the magnitude of its value vector, and its directional alignment with the output of the layer. The methodology involves calculating these factors to provide a more nuanced view of token contributions.
Results indicate that this new metric offers deeper insights into the model's decision-making process, enhancing interpretability. By incorporating geometric considerations, engineers and researchers can better understand and optimize LLM behavior.