The Systems Engineering Approach in Times of Large Language Models

TL;DR

This paper advocates for applying systems engineering principles to effectively integrate Large Language Models into complex socio-technical systems, emphasizing problem prioritization and contextual understanding.

Contribution

It introduces the challenges posed by LLMs and surveys existing systems research efforts, highlighting how systems engineering can facilitate their adoption.

Findings

Systems engineering principles support addressing LLM challenges.

Prioritizing problems and understanding context are crucial for LLM integration.

Survey of research efforts reveals gaps and future directions.

Abstract

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.