Abstract
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.- Anthology ID:
- 2023.findings-acl.408
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6519–6534
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.408
- DOI:
- 10.18653/v1/2023.findings-acl.408
- Cite (ACL):
- Dingjun Wu, Jing Zhang, and Xinmei Huang. 2023. Chain of Thought Prompting Elicits Knowledge Augmentation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6519–6534, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Chain of Thought Prompting Elicits Knowledge Augmentation (Wu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.findings-acl.408.pdf