arXiv 2412.040932024-12-05cs.AI

智能体大语言模型系统的实践考量

Practical Considerations for Agentic LLM Systems

University of Edinburgh（爱丁堡大学）

Chris Sypherd, Vaishak Belle

备注：: 15 pages, 3 figures, 1 table

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文针对LLM智能体实现的挑战，结合应用范式梳理研究社区的实践考量，将成果归纳为规划、记忆、工具和控制流四类，为构建稳健LLM智能体提供背景支持。

摘要：

近年来，随着大语言模型（LLMs）能力的增强，其作为自主智能体底层模型的应用兴趣也日益浓厚。尽管LLMs在自然语言领域展现出涌现能力和广泛专业知识，但其内在的不可预测性使LLM智能体的实现颇具挑战，导致相关研究与这类系统的实际部署之间存在差距。为弥合这一差距，本文结合成熟的应用范式，梳理了研究社区的可操作见解与考量，以支持构建并助力稳健LLM智能体的知情部署。具体而言，我们基于应用导向文献中的常见实践，将相关研究成果归纳为规划、记忆、工具和控制流四大类别，并强调设计面向实际应用的智能体LLM时需注意的实践考量，如处理随机性和高效管理资源。虽然我们未开展实证评估，但为讨论学术界和工业界中智能体LLM设计的关键方面提供了必要背景。

英文摘要：

As the strength of Large Language Models (LLMs) has grown over recent years, so too has interest in their use as the underlying models for autonomous agents. Although LLMs demonstrate emergent abilities and broad expertise across natural language domains, their inherent unpredictability makes the implementation of LLM agents challenging, resulting in a gap between related research and the real-world implementation of such systems. To bridge this gap, this paper frames actionable insights and considerations from the research community in the context of established application paradigms to enable the construction and facilitate the informed deployment of robust LLM agents. Namely, we position relevant research findings into four broad categories--Planning, Memory, Tools, and Control Flow--based on common practices in application-focused literature and highlight practical considerations to make when designing agentic LLMs for real-world applications, such as handling stochasticity and managing resources efficiently. While we do not conduct empirical evaluations, we do provide the necessary background for discussing critical aspects of agentic LLM designs, both in academia and industry.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

AI总结：

摘要：

英文摘要：

相关论文