HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
Jiuding Sun Jing Huang Sidharth Baskaran Karel D'Oosterlinck Christopher Potts Michael Sklar Atticus Geiger
Mechanistic interpretability has made great strides in identifying neural network features (e.g., directions in hidden activation space) that mediate concepts (e.g., *the birth year of a Nobel laureate*) and enable predictable manipulation. Distributed alignment search (DAS) leverages supervision from counterfactual data to learn concept features within hidden states, but DAS assumes we can afford to conduct a brute force search over potential feature locations. To address this, we present HyperDAS, a transformer-based hypernetwork architecture that (1) automatically locates the token-positions of the residual stream that a concept is realized in and (2) learns features of those residual stream vectors for the concept. In experiments with Llama3-8B, HyperDAS achieves state-of-the-art performance on the RAVEL benchmark for disentangling concepts in hidden states. In addition, we review the design decisions we made to mitigate the concern that HyperDAS (like all powerful interpretabilty methods) might inject new information into the target model rather than faithfully interpreting it.
mechanistic interpretability, causal abstraction, hypernetwork
HyperDAS, a transformer-based hypernetwork, enhances interpretability by pinpointing and learning concept-specific features in neural networks' residual streams.