@inproceedings{qian-etal-2023-creator,
title = "{CREATOR}: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models",
author = "Qian, Cheng and
Han, Chi and
Fung, Yi and
Qin, Yujia and
Liu, Zhiyuan and
Ji, Heng",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.462",
doi = "10.18653/v1/2023.findings-emnlp.462",
pages = "6922--6939",
abstract = "Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning, particularly when both planning and execution are involved. To overcome these limitations, we propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization. CREATOR disentangles abstract tool creation and concrete decision execution, resulting in improved performance. We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems and diverse tabular contents. Remarkably, CREATOR outperforms existing chain-of-thought, program-of-thought, and tool-using baselines. Additionally, we introduce the Creation Challenge dataset, featuring 2K diverse questions, to emphasize the necessity and benefits of LLMs{'} tool creation ability. Further research demonstrates that leveraging LLMs as tool creators facilitates knowledge transfer, and LLMs exhibit varying levels of tool creation abilities, enabling them to adapt to diverse situations. The tool creation ability revolutionizes the LLM{'}s problem-solving paradigm, driving us closer to the next frontier of artificial intelligence.",
}
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<abstract>Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning, particularly when both planning and execution are involved. To overcome these limitations, we propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization. CREATOR disentangles abstract tool creation and concrete decision execution, resulting in improved performance. We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems and diverse tabular contents. Remarkably, CREATOR outperforms existing chain-of-thought, program-of-thought, and tool-using baselines. Additionally, we introduce the Creation Challenge dataset, featuring 2K diverse questions, to emphasize the necessity and benefits of LLMs’ tool creation ability. Further research demonstrates that leveraging LLMs as tool creators facilitates knowledge transfer, and LLMs exhibit varying levels of tool creation abilities, enabling them to adapt to diverse situations. The tool creation ability revolutionizes the LLM’s problem-solving paradigm, driving us closer to the next frontier of artificial intelligence.</abstract>
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%0 Conference Proceedings
%T CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models
%A Qian, Cheng
%A Han, Chi
%A Fung, Yi
%A Qin, Yujia
%A Liu, Zhiyuan
%A Ji, Heng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F qian-etal-2023-creator
%X Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning, particularly when both planning and execution are involved. To overcome these limitations, we propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization. CREATOR disentangles abstract tool creation and concrete decision execution, resulting in improved performance. We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems and diverse tabular contents. Remarkably, CREATOR outperforms existing chain-of-thought, program-of-thought, and tool-using baselines. Additionally, we introduce the Creation Challenge dataset, featuring 2K diverse questions, to emphasize the necessity and benefits of LLMs’ tool creation ability. Further research demonstrates that leveraging LLMs as tool creators facilitates knowledge transfer, and LLMs exhibit varying levels of tool creation abilities, enabling them to adapt to diverse situations. The tool creation ability revolutionizes the LLM’s problem-solving paradigm, driving us closer to the next frontier of artificial intelligence.
%R 10.18653/v1/2023.findings-emnlp.462
%U https://aclanthology.org/2023.findings-emnlp.462
%U https://doi.org/10.18653/v1/2023.findings-emnlp.462
%P 6922-6939
Markdown (Informal)
[CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models](https://aclanthology.org/2023.findings-emnlp.462) (Qian et al., Findings 2023)
ACL