Chain of Thought Prompting Elicits Knowledge Augmentation

作者：Di Wu, Jing Zhang, Xinmei Huang · 发表于：Annual Meeting of the Association for Computational Linguistics · 年份：2023 · DOI：10.48550/arxiv.2307.01640 · 被引用次数：53 · 研究领域：Computer Science

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.