面向大语言模型驱动的智能体的计划复用机制

A Plan Reuse Mechanism for LLM-Driven Agent

将大语言模型集成到个人助手中(如小爱同学、蓝心小V等)能有效提升个人助手与人类交互、解决复杂问题、管理物联网设备等能力,这类助手也被称为大模型驱动的智能体,也可称其为智能体.智能体接收到用户请求后,首先调用大模型生成计划,之后调用各类工具执行计划并将响应返回给用户.上述过程中,智能体使用大模型生成计划的延迟可达数十秒,十分影响用户体验.对真实数据的分析显示,智能体接收到的请求中约有30%是相同或相似的,此类请求可复用先前生成的计划,以降低智能体响应延迟.然而,直接对请求原始文本进行相似度评估难以准确界定智能体接收到的请求文本间的相似性.此外,自然语言表达的多样性和大模型生成的非结构化计划文本导致难以对计划进行有效复用.针对上述问题,提出并实现了面向大模型驱动的智能体的计划复用机制AgentReuse,通过利用请求文本间语义的相似性和差异性,采用基于意图分类的方法来界定请求间的相似性并实现计划复用.基于真实数据集的实验结果表明,AgentReuse对计划的有效复用率为93%,对请求进行相似性界定的F1分数为0.9718,准确率为0.9459,与不采用复用机制相比,可减少93.12%的延迟.

Integrating large language models(LLMs)into personal assistants,like Xiao Ai and Blue Heart V,effectively enhances their ability to interact with humans,solve complex tasks,and manage IoT devices.Such assistants are also termed LLM-driven agents.Upon receiving user requests,the LLM-driven agent generates plans using an LLM,executes these plans through various tools,and then returns the response to the user.During this process,the latency for generating a plan with an LLM can reach tens of seconds,significantly degrading user experience.Real-world dataset analysis shows that about 30%of the requests received by LLM-driven agents are identical or similar,which allows the reuse of previously generated plans to reduce latency.However,it is difficult to accurately define the similarity between the request texts received by the LLM-driven agent through directly evaluating the original request texts.Moreover,the diverse expressions of natural language and the unstructured format of plan texts make implementing plan reuse challenging.To address these issues,we present and implement a plan reuse mechanism for LLM-driven agents called AgentReuse.AgentReuse leverages the similarities and differences among requests’semantics and uses intent classification to evaluate the similarities between requests and enable the reuse of plans.Experimental results based on a real-world dataset demonstrate that AgentReuse achieves a 93%effective plan reuse rate,an F1 score of 0.9718,and an accuracy of 0.9459 in evaluating request similarities,reducing latency by 93.12%compared with baselines without using the reuse mechanism.