Adaptive In-conversation Team Building for Language Model Agents

• URL: http://arxiv.org/abs/2405.19425v2
• Date: Sat, 28 Sep 2024 08:05:14 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. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods.
• Score: 33.03550687362213
• License:
• 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, allowing for a flexible yet structured approach to problem-solving. 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. Our exploration of different backbone LLM and cost analysis further shows that Captain Agent can improve the conversation quality of weak LLM and achieve competitive performance with extremely low cost, which illuminates the application of multi-agent systems.

Related papers

• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process. We integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms [55.77492625524141]
  EvoAgent is a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm. We show that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents.
  arXiv  Detail & Related papers  (2024-06-20T11:49:23Z)
• Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning [14.635361844362794]
  Smurfs' is a cutting-edge multi-agent framework designed to revolutionize the application of large language models. Smurfs can enhance the model's ability to solve complex tasks at no additional cost.
  arXiv  Detail & Related papers  (2024-05-09T17:49:04Z)
• Challenges Faced by Large Language Models in Solving Multi-Agent Flocking [17.081075782529098]
  Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation. Recently, large language models (LLMs) have displayed an impressive ability to solve various collaboration tasks as individual decision-makers. This paper discusses the challenges LLMs face in multi-agent flocking and suggests areas for future improvement.
  arXiv  Detail & Related papers  (2024-04-06T22:34:07Z)
• Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning [56.82041895921434]
  Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities. When used as agents for dealing with complex problems in the real world, their performance is far inferior to large commercial models such as ChatGPT and GPT-4.
  arXiv  Detail & Related papers  (2024-03-29T03:48:12Z)
• AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System [91.41155892086252]
  We open-source a new AI agent library, AgentLite, which simplifies research investigation into LLM agents. AgentLite is a task-oriented framework designed to enhance the ability of agents to break down tasks. We introduce multiple practical applications developed with AgentLite to demonstrate its convenience and flexibility.
  arXiv  Detail & Related papers  (2024-02-23T06:25:20Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.