基於LLM的複合人工智慧系統優化：一項調查

LLM-based Optimization of Compound AI Systems: A Survey

October 21, 2024

作者: Matthieu Lin, Jenny Sheng, Andrew Zhao, Shenzhi Wang, Yang Yue, Yiran Wu, Huan Liu, Jun Liu, Gao Huang, Yong-Jin Liu

cs.AI

摘要

在複合人工智慧系統中，組件如LLM調用、檢索器、代碼解釋器或工具是相互連接的。系統的行為主要由指令或工具定義等參數驅動。最近的進展使得可以使用LLM對這些參數進行端到端的優化。值得注意的是，利用LLM作為優化器特別高效，因為它避免了梯度計算，並且能夠生成複雜的代碼和指令。本文介紹了基於LLM對複合人工智慧系統進行優化的原則和新興趨勢的調查。它涵蓋了複合人工智慧系統的典型形式、基於LLM的端到端優化方法，以及對未來方向和更廣泛影響的見解。重要的是，這份調查利用程序分析的概念，提供了一個統一的觀點，說明LLM優化器如何被促使來優化複合人工智慧系統。論文的詳盡清單可在以下網址找到：https://github.com/linyuhongg/LLM-based-Optimization-of-Compound-AI-Systems。

English

In a compound AI system, components such as an LLM call, a retriever, a code interpreter, or tools are interconnected. The system's behavior is primarily driven by parameters such as instructions or tool definitions. Recent advancements enable end-to-end optimization of these parameters using an LLM. Notably, leveraging an LLM as an optimizer is particularly efficient because it avoids gradient computation and can generate complex code and instructions. This paper presents a survey of the principles and emerging trends in LLM-based optimization of compound AI systems. It covers archetypes of compound AI systems, approaches to LLM-based end-to-end optimization, and insights into future directions and broader impacts. Importantly, this survey uses concepts from program analysis to provide a unified view of how an LLM optimizer is prompted to optimize a compound AI system. The exhaustive list of paper is provided at https://github.com/linyuhongg/LLM-based-Optimization-of-Compound-AI-Systems.