Speakers
Description
In the past two years generative AI models have seen an explosion in popularity with many models being published [1]. Text-to-Text models such as ChatGPT3 [2], LaMDA [3], and PEER [4] as well as Text-to-Code models such as Codex [5] and AlphaCode [6] have demonstrated abilities to propose high quality solutions to technical tasks. Applications of these technologies to support space systems engineering have been clearly identified [7] and domain-specific language models have been published [8].
This presentation seeks to build on this work by proposing practical applications of Large language Models (LLMs) to assist MBSE activities. Building and maintaining consistent and complete models for complex space systems is a time-consuming task for engineers. Where system complexity is high and requirements number in there 100’s to 1,000’s. LLMs offer a potential avenue to reduce both the manual work involved in modelling complex systems and reduce the risk or modelling error. Such applications include, but are not limited to, generating initial model diagrams (e.g., operational capabilities diagrams) from natural language descriptions, verifying system model adherence to requirements and design best practice, and ensuring model completeness by identifying any missing information (e.g. data links between sub-systems).
References
[1] R. Gozalo-Brizuela and E. C. Garrido-Merchan, “ChatGPT is not all you need. A State of the Art Review of large Generative AI models,” Jan. 2023, doi: 10.48550/arxiv.2301.04655.
[2] OpenAI, “ChatGPT.” https://chat.openai.com/ (accessed Jan. 19, 2023).
[3] R. Thoppilan et al., “LaMDA: Language Models for Dialog Applications,” Jan. 2022, doi: 10.48550/arxiv.2201.08239.
[4] T. Schick et al., “PEER: A Collaborative Language Model,” Aug. 2022, doi: 10.48550/arxiv.2208.11663.
[5] Open AI, “OpenAI Codex,” Aug. 10, 2021. https://openai.com/blog/openai-codex/ (accessed Jan. 19, 2023).
[6] Y. Li et al., “Competition-Level Code Generation with AlphaCode,” Feb. 2022, doi: 10.1126/science.abq1158.
[7] G. Garcia, G. Pruvost, S. Valera, L. Mansilla, and A. Berquand, “Artificial Intelligence and Natural Language Processing to Support Space Systems Engineering,” in Model Based Space Systems and Software Engineering, 2022.
[8] A. Berquand, P. Darm, and A. Riccardi, “Spacetransformers: Language modeling for space systems,” IEEE Access, vol. 9, pp. 133111–133122, 2021, doi: 10.1109/ACCESS.2021.3115659.
