Adaptive In-conversation Team Building for Language Model Agents
- URL: http://arxiv.org/abs/2405.19425v1
- Date: Wed, 29 May 2024 18:08:37 GMT
- Title: Adaptive In-conversation Team Building for Language Model Agents
- Authors: Linxin Song, Jiale Liu, Jieyu Zhang, Shaokun Zhang, Ao Luo, Shijian Wang, Qingyun Wu, Chi Wang,
- Abstract summary: Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks.
Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent.
- Score: 33.03550687362213
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art. It is thus intriguing to answer a critical question: Given a task, how can we build a team of LLM agents to solve it effectively? Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. It dynamically forms and manages teams for each step of a task-solving process, utilizing nested group conversations and reflection to ensure diverse expertise and prevent stereotypical outputs. It allows for a flexible yet structured approach to problem-solving and can help reduce redundancy and enhance output diversity. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods with 21.94% improvement in average accuracy, providing outstanding performance without requiring task-specific prompt engineering.
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