Automated Feature Labeling with Token-Space Gradient Descent
This work addresses the challenge of interpretability in machine learning for researchers, though it is incremental with constraints like single-token labels and simple features.
The paper tackled the problem of feature labeling by proposing a novel approach using gradient descent in token-space, which directly optimizes label representations with a language model as a discriminator, and demonstrated successful convergence to interpretable single-token labels in domains like animal detection and text classification.
We present a novel approach to feature labeling using gradient descent in token-space. While existing methods typically use language models to generate hypotheses about feature meanings, our method directly optimizes label representations by using a language model as a discriminator to predict feature activations. We formulate this as a multi-objective optimization problem in token-space, balancing prediction accuracy, entropy minimization, and linguistic naturalness. Our proof-of-concept experiments demonstrate successful convergence to interpretable single-token labels across diverse domains, including features for detecting animals, mammals, Chinese text, and numbers. Although our current implementation is constrained to single-token labels and relatively simple features, the results suggest that token-space gradient descent could become a valuable addition to the interpretability researcher's toolkit.