Reference Feature Atlases for Mechanistic Auditing of Language Models
A universal atlas approach for standardized interpretability and mechanical auditing across large language models.
Auditing a new language model usually means relearning and reinterpreting its internal features from scratch. We propose a reference feature atlas: a sparse feature library trained once on a reference panel and reused for new targets, which attach by fitting only a linear decoder...
ArXiv CS.AI
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Standardizing internal feature interpretation across different models allows researchers to create a universal coordinate system for auditing complex neural networks. By isolating residuals, this method explicitly highlights novel behaviors that standard models might miss, significantly improving safety and steerability research.
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