Nora Belrose

The tuned lens learns an affine transformation to decode the activations of each layer of a transformer as next-token predictions. This provides insights into how model predictions are refined layer by layer. We validate our method on various autoregressive language models up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens baseline.

We describe an attack on the state-of-the-art Go-playing AI system, KataGo. The adversaries do not win by learning to play Go better than KataGo but instead by tricking KataGo into making serious blunders, demonstrating that even superhuman AI systems may harbor surprising failure modes.

We describe a software package called "imitation" which provides PyTorch implementations of several imitation and reward learning algorithms, including three inverse reinforcement learning algorithms, three imitation learning algorithms, and a preference comparison algorithm.