A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture

May 09, 2023 ยท The Cartographer ยท ๐Ÿ› 2024 IEEE/ACM 3rd International Conference on AI Engineering โ€“ Software Engineering for AI (CAIN)

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
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"Title-pattern auto-detect: A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture"

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Authors Qinghua Lu, Liming Zhu, Xiwei Xu, Yue Liu, Zhenchang Xing, Jon Whittle arXiv ID 2305.05352 Category cs.SE: Software Engineering Cross-listed cs.AI, cs.CL Citations 18 Venue 2024 IEEE/ACM 3rd International Conference on AI Engineering โ€“ Software Engineering for AI (CAIN) Last Checked 2 days ago
Abstract
The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models. It is widely believed that foundation models will serve as the fundamental building blocks for future AI systems. As foundation models are in their early stages, the design of foundation model based systems has not yet been systematically explored. There is limited understanding about the impact of introducing foundation models in software architecture. Therefore, in this paper, we propose a taxonomy of foundation model based systems, which classifies and compares the characteristics of foundation models and design options of foundation model based systems. Our taxonomy comprises three categories: the pretraining and adaptation of foundation models, the architecture design of foundation model based systems, and responsible-AI-by-design. This taxonomy can serve as concrete guidance for making major architectural design decisions when designing foundation model based systems and highlights trade-offs arising from design decisions.
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