随着深度学习和自然语言处理技术的进步,大语言模型(Large language models,LLMs)展现出巨大潜力.尽管如此,它们在处理复杂任务时仍存在局限性,特别是在任务需要结合规划及外部工具调用的场合.面向这一挑战,提出国内首个以军事游戏为背景的中文的复杂任务规划与执行数据集(Complex task planning and execution dataset,CTPaE),以及一个基于LLMs的自主复杂任务规划(Complex task planning,CTP)处理框架AutoPlan.该框架可以对复杂任务进行自主规划得到元任务序列,并使用递进式ReAct提示(Progressive ReAct prompting,PRP)方法对已规划的元任务逐步执行.该框架的有效性通过在CTPaE上的实验及与其他经典算法的比较分析得到了验证.项目地址:https://github.com/LDLINGLINGLING/AutoPlan.
Abstract
With the advancement of deep learning and natural language processing technologies,large language models(LLMs)have shown significant potential.Despite their power,they still face limitations when dealing with complex tasks,especially when the tasks require integrative planning and the invocation of external tools.In re-sponse to this challenge,this paper proposes the first domestic dataset for complex task planning and execution with a military game context,the Chinese complex task planning and execution dataset(CTPaE),and a new frame-work for autonomous complex task planning(CTP)using LLMs named AutoPlan.The framework is capable of autonomously planning complex tasks to generate a sequence of meta-tasks,and employs a progressive ReAct prompting(PRP)method to gradually execute the planned meta-tasks.The effectiveness of the framework has been validated through experiments on the CTPaE and comparative analysis with other classic algorithms.The link of project:https://github.com/LDLINGLINGLING/AutoPlan.
关键词
大语言模型/工具调用/多步推理/深度学习
Key words
Large language models(LLMs)/tool-use/multi-hop reasoning/deep learning
维普
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