Deep Learning Models Also Recall Features

The paper discusses how large language models retrieve facts from their weights, proposing that this phenomenon reflects a broader operation termed feature recall. It argues that a linear projection can be interpreted as retrieving stored information scaled by input activations, and demonstrates that feature recall applies across various architectures, contrasting it with the traditional feature combination paradigm. The authors also explore potential mechanistic identification of feature recall cases and suggest new empirical directions for mechanistic interpretability research.

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