Chain of Thought Prompting Elicits Knowledge Augmentation
Dingjun Wu Jing Zhang Xinmei Huang
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.