用系统工程方法解决大模型落地难题
The Systems Engineering Approach in Times of Large Language Models
- 以问题和场景为导向，而非先看技术
- 梳理了大模型带来的系统性挑战
- 适合关注技术落地的工程师与决策者
利用大语言模型（LLMs）解决关键社会问题，需要将其融入复杂的人-机-社会系统。然而，这类系统的复杂性以及大模型本身的特性，使这一愿景面临挑战。单纯依赖人工智能领域自身难以破解这些难题。相比之下，系统工程方法更适合作为解决方案：它优先关注问题本身及其上下文环境，再考虑其他要素。本文分析了大模型带来的核心挑战，综述了现有系统研究在构建基于AI的系统方面的进展。我们揭示了系统工程原则如何曾成功应对类似问题，并据此提出未来大模型采纳的方向。
原文摘要 · Abstract (English)
Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilitate the adoption of LLMs by prioritising the problems and their context before any other aspects. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems. We reveal how the systems engineering principles have supported addressing similar issues to the ones LLMs pose and discuss our findings to provide future directions for adopting LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。