@inproceedings{inaba-etal-2023-multitool,
title = "{M}ulti{T}ool-{C}o{T}: {GPT}-3 Can Use Multiple External Tools with Chain of Thought Prompting",
author = "Inaba, Tatsuro and
Kiyomaru, Hirokazu and
Cheng, Fei and
Kurohashi, Sadao",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.130",
doi = "10.18653/v1/2023.acl-short.130",
pages = "1522--1532",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting
%A Inaba, Tatsuro
%A Kiyomaru, Hirokazu
%A Cheng, Fei
%A Kurohashi, Sadao
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F inaba-etal-2023-multitool
%X 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.
%R 10.18653/v1/2023.acl-short.130
%U https://aclanthology.org/2023.acl-short.130
%U https://doi.org/10.18653/v1/2023.acl-short.130
%P 1522-1532
Markdown (Informal)
[MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting](https://aclanthology.org/2023.acl-short.130) (Inaba et al., ACL 2023)
ACL