The Systems Engineering Approach in Times of Large Language Models
This work addresses the problem of effectively deploying LLMs in complex real-world systems for stakeholders in engineering and AI, but it is incremental as it builds on existing systems research.
The paper tackles the challenge of integrating Large Language Models (LLMs) into socio-technical systems for societal problems, arguing that systems engineering principles are better suited than AI methods to prioritize context and facilitate adoption.
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.