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While Chain-of-Thought prompting is popular in reasoning tasks, itsapplication to Large Language Models (LLMs) in Natural Language Understanding(NLU) is under-explored. Motivated by multi-step reasoning of LLMs, we proposeCoarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasksinto multiple reasoning steps where LLMs can learn to acquire and leverageessential concepts to solve tasks from different granularities. Moreover, wepropose leveraging semantic-based Abstract Meaning Representation (AMR)structured knowledge as an intermediate step to capture the nuances and diversestructures of utterances, and to understand connections between their varyinglevels of granularity. Our proposed approach is demonstrated effective inassisting the LLMs adapt to the multi-grained NLU tasks under both zero-shotand few-shot multi-domain settings.

CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks