Do language models plan ahead for future tokens?
<|reference_start|>Do language models plan ahead for future tokens?: 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 time step $t$ 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 during 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 constructed 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, though pre-caching increases with model scale.<|reference_end|>
@article{wu2024do,
    title={Do language models plan ahead for future tokens?},
    author={Wilson Wu, John X. Morris, Lionel Levine},
    journal={arXiv preprint arXiv:2404.00859},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00859},
    primaryClass={cs.LG cs.CL}
}