@inproceedings{gao-etal-2024-360,
title = "$360^\circ${REA}: Towards A Reusable Experience Accumulation with $360^\circ$ Assessment for Multi-Agent System",
author = "Gao, Shen and
Li, Hao and
Shi, Zhengliang and
Huang, Chengrui and
Tu, Quan and
Shang, Shuo and
Tian, Zhiliang and
Huang, Minlie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.778",
pages = "13149--13162",
abstract = "Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. Since these agents are all based on the same LLM, only conducting self-evaluation or removing underperforming agents does not substantively enhance the capability of the agents. We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effective approach to improving system performance. In this paper, we propose Reusable Experience Accumulation with $\mathbf{360^\circ}$ Assessment ($360^\circ$REA), a hierarchical multi-agent framework inspired by corporate organizational practices. The framework employs a novel $360^\circ$ performance assessment method for multi-perspective performance evaluation with fine-grained assessment. To enhance the capability of agents in addressing complex tasks, we introduce dual-level experience pool for agents to accumulate experience through fine-grained assessment. Extensive experiments on complex task datasets demonstrate the effectiveness of $360^\circ$REA.",
}
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%0 Conference Proceedings
%T 360°REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
%A Gao, Shen
%A Li, Hao
%A Shi, Zhengliang
%A Huang, Chengrui
%A Tu, Quan
%A Shang, Shuo
%A Tian, Zhiliang
%A Huang, Minlie
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F gao-etal-2024-360
%X Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. Since these agents are all based on the same LLM, only conducting self-evaluation or removing underperforming agents does not substantively enhance the capability of the agents. We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effective approach to improving system performance. In this paper, we propose Reusable Experience Accumulation with \mathbf360° Assessment (360°REA), a hierarchical multi-agent framework inspired by corporate organizational practices. The framework employs a novel 360° performance assessment method for multi-perspective performance evaluation with fine-grained assessment. To enhance the capability of agents in addressing complex tasks, we introduce dual-level experience pool for agents to accumulate experience through fine-grained assessment. Extensive experiments on complex task datasets demonstrate the effectiveness of 360°REA.
%U https://aclanthology.org/2024.findings-acl.778
%P 13149-13162
Markdown (Informal)
[360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System](https://aclanthology.org/2024.findings-acl.778) (Gao et al., Findings 2024)
ACL
- Shen Gao, Hao Li, Zhengliang Shi, Chengrui Huang, Quan Tu, Shuo Shang, Zhiliang Tian, and Minlie Huang. 2024. 360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System. In Findings of the Association for Computational Linguistics ACL 2024, pages 13149–13162, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.