Show Your Work: Scratchpads for Intermediate Computation with Language Models
Abstract
Large pre-trained language models perform remarkably well on tasks that can be done “in one pass”, such as generating realistic text (Brown et al., 2020) or synthesizing computer programs (Chen et al., 2021; Austin et al., 2021). However, they struggle with tasks that require unbounded multi-step computation, such as adding integers (Brown et al., 2020) or executing programs (Austin et al., 2021). Surprisingly, we find that these same models are able to perform complex multistep computations—even in the few-shot regime—when asked to perform the operation “step by step”, showing the results of intermediate computations. In particular, we train Transformers to perform multi-step computations by asking them to emit intermediate computation steps into a “scratchpad”. On a series of increasingly complex tasks ranging from long addition to the execution of arbitrary programs, we show that scratchpads dramatically improve the ability of language models to perform multi-step computations.
Research Areas
