arXiv:2606.07617v1 Announce Type: new 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 indirect, module-mediated effects that arise when the feature is processed by downstream modules, going be
The continuous development in AI necessitates better interpretability tools to understand complex neural networks, especially as models grow in scale and autonomy.
Improved interpretability of sparse autoencoders is crucial for debugging, auditing, and safely deploying advanced AI systems, particularly in sensitive applications.
This research provides a more comprehensive method for understanding how sparse features contribute to model behavior, including indirect effects, making AI models less opaque.
- · AI researchers
- · AI safety organizations
- · Developers of large language models
- · Companies deploying AI in critical infrastructure
- · Developers relying on black-box AI
- · Proprietary AI systems without interpretability tools
AI models become more transparent, allowing for better analysis of their decision-making processes.
Increased trust and adoption of advanced AI systems in domains requiring high assurance and accountability.
New regulatory frameworks and audit requirements emerge for AI, leveraging these advanced interpretability techniques.
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Read at arXiv cs.LG