Chain of Thought Prompting Elicits Knowledge Augmentation
Dingjun Wu, Jing Lin Zhang, Xinmei Huang · 2023
The knowledge-augmented deep learning paradigm refers to a paradigm in which domain knowledge is identified and integrated into deep models.Conventional methods typically employ task-specific approaches to gather external knowledge from various sources.In contrast, large language models are extensively pre-trained and can serve as a comprehensive source of external knowledge.In this paper, we propose CoT-KA, a Chain-of-Thought-based method that augments knowledge for deep learning.CoT-KA avoids the need for additional knowledge retrieval or knowledge reasoning models, as required in conventional augmentation methods.Our results demonstrate that CoT-KA outperforms both pure CoT-based methods and the non-augmented method across the majority of eleven publicly available benchmarks for various reasoning tasks 1 .