Meta-Task Planning for Language Agents
<|reference_start|>Meta-Task Planning for Language Agents: 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.<|reference_end|>
@article{zhang2024meta-task,
    title={Meta-Task Planning for Language Agents},
    author={Cong Zhang, Derrick Goh Xin Deik, Dexun Li, Hao Zhang, Yong Liu},
    journal={arXiv preprint arXiv:2405.16510},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16510},
    primaryClass={cs.AI cs.CL cs.LG}
}