@inproceedings{shi-etal-2024-learning,
title = "Learning to Use Tools via Cooperative and Interactive Agents",
author = "Shi, Zhengliang and
Gao, Shen and
Chen, Xiuyi and
Feng, Yue and
Yan, Lingyong and
Shi, Haibo and
Yin, Dawei and
Ren, Pengjie and
Verberne, Suzan and
Ren, Zhaochun",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.624/",
doi = "10.18653/v1/2024.findings-emnlp.624",
pages = "10642--10657",
abstract = "Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively select and execute tools, thereafter incorporating execution results into the next action prediction. Despite their progress, these methods suffer from performance degradation when addressing practical tasks due to: (1) the pre-defined pipeline with restricted flexibility to calibrate incorrect actions, and (2) the struggle to adapt a general LLM-based agent to perform a variety of specialized actions. To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. ConAgents introduces two communication protocols to enable the flexible cooperation of agents. To effectively generalize the ConAgents into open-source models, we also propose specialized action distillation, enhancing their ability to perform specialized actions in our framework. Our extensive experiments on three datasets show that the LLMs, when equipped with the ConAgents, outperform baselines with substantial improvement (i.e., up to 14{\%} higher success rate)."
}
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<abstract>Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively select and execute tools, thereafter incorporating execution results into the next action prediction. Despite their progress, these methods suffer from performance degradation when addressing practical tasks due to: (1) the pre-defined pipeline with restricted flexibility to calibrate incorrect actions, and (2) the struggle to adapt a general LLM-based agent to perform a variety of specialized actions. To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. ConAgents introduces two communication protocols to enable the flexible cooperation of agents. To effectively generalize the ConAgents into open-source models, we also propose specialized action distillation, enhancing their ability to perform specialized actions in our framework. Our extensive experiments on three datasets show that the LLMs, when equipped with the ConAgents, outperform baselines with substantial improvement (i.e., up to 14% higher success rate).</abstract>
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%0 Conference Proceedings
%T Learning to Use Tools via Cooperative and Interactive Agents
%A Shi, Zhengliang
%A Gao, Shen
%A Chen, Xiuyi
%A Feng, Yue
%A Yan, Lingyong
%A Shi, Haibo
%A Yin, Dawei
%A Ren, Pengjie
%A Verberne, Suzan
%A Ren, Zhaochun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F shi-etal-2024-learning
%X Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively select and execute tools, thereafter incorporating execution results into the next action prediction. Despite their progress, these methods suffer from performance degradation when addressing practical tasks due to: (1) the pre-defined pipeline with restricted flexibility to calibrate incorrect actions, and (2) the struggle to adapt a general LLM-based agent to perform a variety of specialized actions. To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. ConAgents introduces two communication protocols to enable the flexible cooperation of agents. To effectively generalize the ConAgents into open-source models, we also propose specialized action distillation, enhancing their ability to perform specialized actions in our framework. Our extensive experiments on three datasets show that the LLMs, when equipped with the ConAgents, outperform baselines with substantial improvement (i.e., up to 14% higher success rate).
%R 10.18653/v1/2024.findings-emnlp.624
%U https://aclanthology.org/2024.findings-emnlp.624/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.624
%P 10642-10657
Markdown (Informal)
[Learning to Use Tools via Cooperative and Interactive Agents](https://aclanthology.org/2024.findings-emnlp.624/) (Shi et al., Findings 2024)
ACL
- Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Pengjie Ren, Suzan Verberne, and Zhaochun Ren. 2024. Learning to Use Tools via Cooperative and Interactive Agents. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10642–10657, Miami, Florida, USA. Association for Computational Linguistics.