mlsanityData to 7 Oct 2026

The Systems Engineering Approach in Times of Large Language Models

arXiv 2411.09050 ↗First posted 13 Nov 2024cs.AI cs.CY cs.SEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 44 references that are in the map. First posted on arXiv 13 Nov 2024.

Builds on

12 of 44 references.

References · 12

  1. 2019Green AIcited by 620
  2. 2023Explainability for Large Language Models: A Surveycited by 331
  3. 2023Auditing large language models: a three-layered approachcited by 135
  4. 2023Generative AIcited by 119
  5. 2020Technology Readiness Levels for AI & MLcited by 30
  6. 2024Generative AI and Process Systems Engineering: The Next Frontiercited by 8
  7. 2024Requirements are All You Need: The Final Frontier for End-User Software Engineeringcited by 5
  8. 2022Core and Periphery as Closed-System Precepts for Engineering General Intelligencecited by 3
  9. 2023Machine Learning Systems: A Survey from a Data-Oriented Perspectivecited by 3
  10. 2022Five Ps: Leverage Zones Towards Responsible AIcited by 2
  11. 2024Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptationcited by 2
  12. 2021Understanding and Modeling AI-Intensive System Developmentcited by 1

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