MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting

Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, Sadao Kurohashi


Abstract
Large language models (LLMs) have achieved impressive performance on various reasoning tasks. To further improve the performance, we propose MultiTool-CoT, a novel framework that leverages chain-of-thought (CoT) prompting to incorporate multiple external tools, such as a calculator and a knowledge retriever, during the reasoning process. We apply MultiTool-CoT to the Task 2 dataset of NumGLUE, which requires both numerical reasoning and domain-specific knowledge. The experiments show that our method significantly outperforms strong baselines and achieves state-of-the-art performance.
Anthology ID:
2023.acl-short.130
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1522–1532
Language:
URL:
https://aclanthology.org/2023.acl-short.130
DOI:
10.18653/v1/2023.acl-short.130
Bibkey:
Cite (ACL):
Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, and Sadao Kurohashi. 2023. MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1522–1532, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting (Inaba et al., ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-short.130.pdf
Video:
 https://aclanthology.org/2023.acl-short.130.mp4