Do language models plan ahead for future tokens?
Wilson Wu John X. Morris Lionel Levine
Do transformers "think ahead" during inference at a given position? It is
known transformers prepare information in the hidden states of the forward pass
at $t$ that is then used in future forward passes $t+\tau$. We posit two
explanations for this phenomenon: pre-caching, in which off-diagonal gradient
terms present in training result in the model computing features at $t$
irrelevant to the present inference task but useful for the future, and
breadcrumbs, in which features most relevant to time step $t$ are already the
same as those that would most benefit inference at time $t+\tau$. We test these
hypotheses by training language models without propagating gradients to past
timesteps, a scheme we formalize as myopic training. In a synthetic data
setting, we find clear evidence for pre-caching. In the autoregressive language
modeling setting, our experiments are more suggestive of the breadcrumbs
hypothesis.