Interpretability

Inspect Transformer Predictions with the Logit Lens

A transformer language model produces its next-token prediction only after many layers of computation. When that prediction is wrong or surprising, developers often want a more specific question answered: how did the model’s candidate tokens change as the input moved through the network? The logit lens is a simple interpretability technique for exploring that question. Instead of waiting for the final layer, it takes an intermediate representation and passes it through the model’s final decoding machinery to obtain vocabulary logits. Repeating this across layers gives a rough view of how token predictions evolve with depth.