Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks

• URL: http://arxiv.org/abs/2407.09893v2
• Date: Mon, 26 Aug 2024 07:54:27 GMT
• Title: Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
• Authors: Shengbin Yue, Siyuan Wang, Wei Chen, Xuanjing Huang, Zhongyu Wei,
• Abstract summary: Large Language Models (LLMs) have led to significant breakthroughs in various natural language processing tasks. generating factually consistent responses in knowledge-intensive scenarios remains a challenge. This paper introduces SMART, a novel multi-agent framework that leverages external knowledge to enhance the interpretability and factual consistency of LLM-generated responses.
• Score: 44.42989163847349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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