Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
📰 ArXiv cs.AI
Learn to interpret sparse key-value features using Query Lens, enabling more comprehensive understanding of autoencoder features
Action Steps
- Apply Query Lens to your sparse autoencoder model to identify key features and their corresponding values
- Configure the encoder-side key features and decoder-side value features to jointly consider their effects
- Test the interpretation of sparse features using Query Lens and compare with existing methods
- Run experiments to evaluate the faithfulness of Query Lens in characterizing sparse features
- Use Query Lens to identify indirect effects of sparse features on model outputs
Who Needs to Know This
Machine learning engineers and researchers can benefit from this technique to improve the interpretability of their models, particularly when working with sparse autoencoders
Key Insight
💡 Query Lens extends Logit Lens to provide a more comprehensive interpretation of sparse features by considering both encoder-side key features and decoder-side value features
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🔍 Introducing Query Lens: a technique to interpret sparse key-value features in autoencoders, enabling more comprehensive model understanding #MachineLearning #Interpretability
Key Takeaways
Learn to interpret sparse key-value features using Query Lens, enabling more comprehensive understanding of autoencoder features
Full Article
Title: Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects
Abstract:
arXiv:2606.07617v1 Announce Type: cross Abstract: While sparse autoencoders provide features more interpretable than individual neurons, reliably characterizing them remains challenging. We propose Query Lens, which extends Logit Lens to enable more comprehensive and faithful interpretations of sparse features. By jointly considering encoder-side key features and decoder-side value features, we identify both the inputs that activate a feature and the outputs it promotes. We also account for indi
Abstract:
arXiv:2606.07617v1 Announce Type: cross Abstract: While sparse autoencoders provide features more interpretable than individual neurons, reliably characterizing them remains challenging. We propose Query Lens, which extends Logit Lens to enable more comprehensive and faithful interpretations of sparse features. By jointly considering encoder-side key features and decoder-side value features, we identify both the inputs that activate a feature and the outputs it promotes. We also account for indi
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