Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions. This inadequacy primarily stems from the lack of built-in action knowledge in language agents, which fails to effectively guide the planning trajectories during task solving and results in planning hallucination. To address this issue, we introduce KnowAgent, a novel approach designed to enhance the planning capabilities of LLMs by incorporating explicit action knowledge. Specifically, KnowAgent employs an action knowledge base and a knowledgeable self-learning strategy to constrain the action path during planning, enabling more reasonable trajectory synthesis, and thereby enhancing the planning performance of language agents. Experimental results on HotpotQA and ALFWorld based on various backbone models demonstrate that KnowAgent can achieve comparable or superior performance to existing baselines. Further analysis indicates the effectiveness of KnowAgent in terms of planning hallucinations mitigation. Code is available in https://github.com/zjunlp/KnowAgent.

翻译：大型语言模型（LLMs）在复杂推理任务中展现出巨大潜力，但在应对更具挑战性的场景时仍显不足，尤其是在通过生成可执行动作与环境交互的过程中。这一缺陷主要源于语言智能体缺乏内建动作知识，导致其在任务求解过程中无法有效引导规划轨迹，进而引发规划幻觉。为应对该问题，我们提出KnowAgent——一种通过引入显式动作知识增强LLM规划能力的新方法。具体而言，KnowAgent通过构建动作知识库并采用知识驱动的自学习策略，在规划阶段约束动作路径，促进更合理的轨迹合成，从而提升语言智能体的规划性能。基于多种骨干模型在HotpotQA和ALFWorld上的实验结果表明，KnowAgent能够取得与现有基线方法相当甚至更优的性能。进一步分析揭示了KnowAgent在缓解规划幻觉方面的有效性。代码已开源：https://github.com/zjunlp/KnowAgent。