Publications
Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
Concept Influence attributes model behaviors to semantic directions (like linear probes or sparse autoencoder features) rather than individual test examples, improving identification of the training data that disproportionately drive unintended behaviors. Simple first-order approximations match or outperform standard influence functions while achieving over 20× computational speedups, though they degrade under significant distribution shifts.
News
Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
Concept Influence attributes model behaviors to semantic directions (like linear probes or sparse autoencoder features) rather than individual test examples, improving identification of the training data that disproportionately drive unintended behaviors. Simple first-order approximations match or outperform standard influence functions while achieving over 20× computational speedups, though they degrade under significant distribution shifts.