CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine
Chain-of-Thought Prompting for Multi-domain NLU Tasks
- URL: http://arxiv.org/abs/2310.14623v1
- Date: Mon, 23 Oct 2023 06:54:51 GMT
- Title: CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine
Chain-of-Thought Prompting for Multi-domain NLU Tasks
- Authors: Hoang H. Nguyen, Ye Liu, Chenwei Zhang, Tao Zhang, Philip S. Yu
- Abstract summary: Chain-of-Thought prompting is popular in reasoning tasks, but its application to Natural Language Understanding (NLU) is under-explored.
Motivated by multi-step reasoning of Large Language Models (LLMs), we propose Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach.
- Score: 46.862929778121675
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While Chain-of-Thought prompting is popular in reasoning tasks, its
application to Large Language Models (LLMs) in Natural Language Understanding
(NLU) is under-explored. Motivated by multi-step reasoning of LLMs, we propose
Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasks
into multiple reasoning steps where LLMs can learn to acquire and leverage
essential concepts to solve tasks from different granularities. Moreover, we
propose leveraging semantic-based Abstract Meaning Representation (AMR)
structured knowledge as an intermediate step to capture the nuances and diverse
structures of utterances, and to understand connections between their varying
levels of granularity. Our proposed approach is demonstrated effective in
assisting the LLMs adapt to the multi-grained NLU tasks under both zero-shot
and few-shot multi-domain settings.
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