While Pre-trained Language Models (PLMs) internalize a great amount of world
knowledge, they have been shown incapable of recalling these knowledge to solve
tasks requiring complex & multi-step reasoning. Similar to how humans develop a
"chain of thought" for these tasks, how can we equip PLMs with such abilities?
In this work, we explore an iterative prompting framework, a new prompting
paradigm which progressively elicits relevant knowledge from PLMs for
multi-step inference. We identify key limitations of existing prompting
methods, namely they are either restricted to queries with a single
identifiable relation/predicate, or being agnostic to input contexts, which
makes it difficult to capture variabilities across different inference steps.
We propose an iterative context-aware prompter, which addresses these
limitations by learning to dynamically synthesize prompts conditioned on the
current step's contexts. Experiments on three datasets involving multi-step
reasoning show the effectiveness of the iterative scheme and the context-aware
prompter design.

本研究探索了一个迭代提示框架，用于让预训练语言模型具有多步推理能力，我们提出了一种基于上下文的迭代提示器，可以动态地合成提示，以捕捉不同推理步骤之间的变化。实验证明，该方法在多步推理方面具有很好的效果。