一种用于在基于大语言模型的软件系统中开发与增强能力的分层架构
A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems
AI总结：
本文提出一种分层架构来组织基于大语言模型的软件系统开发，通过将能力与不同层次对齐，帮助开发者系统化选择合适技术，以平衡工程复杂性、可扩展性和成本，从而提升系统稳健性与可扩展性。
摘要：
人们已经付出大量努力，将大语言模型（LLMs）的应用扩展到基础语言任务之外。虽然LLMs的泛化能力和多功能性促成了广泛采用，但应用开发中不断变化的需求往往超出其原生能力。满足这些需求可能涉及多种方法，例如通过调整推理温度或使用激发创造力的提示来增强创造性。选择正确的方法至关重要，因为不同方法会在工程复杂性、可扩展性和运营成本之间带来权衡。本文提出一种分层架构，将LLM软件系统开发组织为不同的层次，每一层都具有特定属性。通过将能力与这些层次对齐，该框架鼓励以有效且高效的方式系统化地实现能力，从而最终支持所期望的功能和质量。通过实际案例研究，我们展示了该框架的实用性。这项工作为开发者在基于LLM的软件系统开发中选择合适技术提供了可操作的见解，从而促进稳健性和可扩展性。
英文摘要：
Significant efforts has been made to expand the use of Large Language Models (LLMs) beyond basic language tasks. While the generalizability and versatility of LLMs have enabled widespread adoption, evolving demands in application development often exceed their native capabilities. Meeting these demands may involve a diverse set of methods, such as enhancing creativity through either inference temperature adjustments or creativity-provoking prompts. Selecting the right approach is critical, as different methods lead to trade-offs in engineering complexity, scalability, and operational costs. This paper introduces a layered architecture that organizes LLM software system development into distinct layers, each characterized by specific attributes. By aligning capabilities with these layers, the framework encourages the systematic implementation of capabilities in effective and efficient ways that ultimately supports desired functionalities and qualities. Through practical case studies, we illustrate the utility of the framework. This work offers developers actionable insights for selecting suitable technologies in LLM-based software system development, promoting robustness and scalability.
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。
