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        <title>Chain of Thought Prompting Elicits Knowledge Augmentation</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.408</identifier>
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%0 Conference Proceedings
%T Chain of Thought Prompting Elicits Knowledge Augmentation
%A Wu, Dingjun
%A Zhang, Jing
%A Huang, Xinmei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wu-etal-2023-chain
%X 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.
%R 10.18653/v1/2023.findings-acl.408
%U https://aclanthology.org/2023.findings-acl.408
%U https://doi.org/10.18653/v1/2023.findings-acl.408
%P 6519-6534

Markdown (Informal)

[Chain of Thought Prompting Elicits Knowledge Augmentation](https://aclanthology.org/2023.findings-acl.408) (Wu et al., Findings 2023)

• Chain of Thought Prompting Elicits Knowledge Augmentation (Wu et al., Findings 2023)

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.