Meta-Task Planning for Language Agents

• URL: http://arxiv.org/abs/2405.16510v3
• Date: Thu, 30 May 2024 12:40:06 GMT
• Title: Meta-Task Planning for Language Agents
• Authors: Cong Zhang, Derrick Goh Xin Deik, Dexun Li, Hao Zhang, Yong Liu,
• Abstract summary: Large language model-based agents (LLM agents) have emerged as a promising paradigm for achieving artificial general intelligence (AGI) This paper introduces Meta-Task Planning (MTP), a zero-shot methodology for collaborative LLM-based multi-agent systems. MTP achieved an average $sim40%$ success rate on TravelPlanner, significantly higher than the state-of-the-art (SOTA) baseline.
• Score: 13.550774629515843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large language model-based agents (LLM agents) have emerged as a promising paradigm for achieving artificial general intelligence (AGI) due to their superior reasoning and generalization capabilities. Effective planning is crucial for the success of LLM agents in real-world tasks, making it a highly pursued topic in the community. Current planning methods typically translate tasks into executable action sequences. However, determining a feasible or optimal sequence for complex tasks at fine granularity, which often requires compositing long chains of heterogeneous actions, remains challenging. This paper introduces Meta-Task Planning (MTP), a zero-shot methodology for collaborative LLM-based multi-agent systems that simplifies complex task planning by decomposing it into a hierarchy of subordinate tasks, or meta-tasks. Each meta-task is then mapped into executable actions. MTP was assessed on two rigorous benchmarks, TravelPlanner and API-Bank. Notably, MTP achieved an average $\sim40\%$ success rate on TravelPlanner, significantly higher than the state-of-the-art (SOTA) baseline ($2.92\%$), and outperforming $LLM_{api}$-4 with ReAct on API-Bank by $\sim14\%$, showing the immense potential of integrating LLM with multi-agent systems.

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