Interesting! Glad to see our method being utilized in future research.
Do you have any metrics (e.g., Explained Variance or CE loss difference) on how SAEs trained on a specific dataset perform when applied to others? I suspect that if there is a small gap between the explained variance on the training dataset and other datasets, we might infer that, even though there’s no one-to-one correspondence between features learned across datasets, the combination of features retains a degree of similarity.
Additionally, it would be intriguing to investigate whether ... (read more)
While authors claim that their approach is fundamentally different from transcoders, from my perspective, it addresses thesame issue: finding interpretable circuits. I agree that it modulates MLPs in different ways (e.g., connecting sparse inputs and sparse outputs, rather than modulating MLP directly). However, it would be greatto see thedifference between circuits identified by transcoders and those found by JSAE.
We also discuss this similarity in Appendix F of our recent work (where transition Tis analogous to f), though we do not consider gradient... (read more)
Interesting! Glad to see our method being utilized in future research.
Do you have any metrics (e.g., Explained Variance or CE loss difference) on how SAEs trained on a specific dataset perform when applied to others? I suspect that if there is a small gap between the explained variance on the training dataset and other datasets, we might infer that, even though there’s no one-to-one correspondence between features learned across datasets, the combination of features retains a degree of similarity.
Additionally, it would be intriguing to investigate whether ... (read more)