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Scaling language models to larger and deeper sizes has led to significantboosts in performance. Even though the size of these models limits theirapplication in compute-constrained environments, the race to continuallydevelop ever larger and deeper foundational models is underway. At the sametime -- regardless of the model size -- task-specific techniques continue toplay a pivotal role in achieving optimal downstream performance. One of thesetechniques, called Chain-of-Thought (CoT), is particularly interesting since,as we point out in this work, it resembles employing a deeper transformerthrough re-applying the model multiple times. However, a key subtlety incomputing the attention of past tokens differentiates CoT from simply applyingthe model several times. Based on this insight, we propose CoTFormer, a novelarchitecture which closely mimics CoT at the token level, allowing us to obtainsignificantly improved accuracies close to much larger models. While applyingCoT introduces additional computation costs, we compensate for it by leveragingCoTFormer's special compatibility with token-wise variable depth. Through acompute adaptive model -- which automatically allocates the compute to tokensthat need it most -- we show that it is possible to reduce the computation costsignificantly without any reduction in accuracy, and with further compute costreductions possible while maintaining a competitive accuracy.

CoTFormer: A Chain-of-Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference