Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
<|reference_start|>Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks: Recent advancements in Large Language Models (LLMs) have led to significant breakthroughs in various natural language processing tasks. However, generating factually consistent responses in knowledge-intensive scenarios remains a challenge due to issues such as hallucination, difficulty in acquiring long-tailed knowledge, and limited memory expansion. This paper introduces SMART, a novel multi-agent framework that leverages external knowledge to enhance the interpretability and factual consistency of LLM-generated responses. SMART comprises four specialized agents, each performing a specific sub-trajectory action to navigate complex knowledge-intensive tasks. We propose a multi-agent co-training paradigm, Long-Short Trajectory Learning, which ensures synergistic collaboration among agents while maintaining fine-grained execution by each agent. Extensive experiments on five knowledge-intensive tasks demonstrate SMART's superior performance compared to widely adopted knowledge internalization and knowledge enhancement methods. Our framework can extend beyond knowledge-intensive tasks to more complex scenarios. Our code is available at https://github.com/yueshengbin/SMART.<|reference_end|>
@article{yue2024synergistic,
    title={Synergistic Multi-Agent Framework with Trajectory Learning for
  Knowledge-Intensive Tasks},
    author={Shengbin Yue and Siyuan Wang and Wei Chen and Xuanjing Huang and
  Zhongyu Wei},
    journal={arXiv preprint arXiv:2407.09893},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09893},
    primaryClass={cs.CL}
}