Transcoders Beat Sparse Autoencoders for Interpretability
This work addresses the challenge of improving interpretability in AI systems for researchers and practitioners, though it appears incremental as it builds on existing methods like sparse autoencoders.
The paper tackled the problem of extracting interpretable features from deep neural networks by comparing transcoders to sparse autoencoders, finding that transcoder features are significantly more interpretable and that skip transcoders achieve lower reconstruction loss without compromising interpretability.
Sparse autoencoders (SAEs) extract human-interpretable features from deep neural networks by transforming their activations into a sparse, higher dimensional latent space, and then reconstructing the activations from these latents. Transcoders are similar to SAEs, but they are trained to reconstruct the output of a component of a deep network given its input. In this work, we compare the features found by transcoders and SAEs trained on the same model and data, finding that transcoder features are significantly more interpretable. We also propose skip transcoders, which add an affine skip connection to the transcoder architecture, and show that these achieve lower reconstruction loss with no effect on interpretability.