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While Pre-trained Language Models (PLMs) internalize a great amount of worldknowledge, they have been shown incapable of recalling these knowledge to solvetasks 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 promptingparadigm which progressively elicits relevant knowledge from PLMs formulti-step inference. We identify key limitations of existing promptingmethods, namely they are either restricted to queries with a singleidentifiable relation/predicate, or being agnostic to input contexts, whichmakes it difficult to capture variabilities across different inference steps.We propose an iterative context-aware prompter, which addresses theselimitations by learning to dynamically synthesize prompts conditioned on thecurrent step's contexts. Experiments on three datasets involving multi-stepreasoning show the effectiveness of the iterative scheme and the context-awareprompter design.

Iteratively Prompt Pre-trained Language Models for Chain of Thought