LLMs enhance semantic alignment in collaborative systems engineering using SysML v2

A new paper proposes a method for using Large Language Models (LLMs), specifically GPT-based models, to improve semantic alignment in collaborative Model-Based Systems Engineering (MBSE). The approach leverages SysML v2's enhanced structural modularity and formal semantics to facilitate interoperable modeling. The core contribution involves an iterative process of developing alignment strategies and interaction prompts, which include model extraction, semantic matching, and verification, ultimately supporting traceable integration. AI

IMPACT This research could streamline collaboration in complex engineering projects by improving model interoperability.

RANK_REASON The cluster contains a research paper detailing a novel approach using LLMs for semantic alignment in MBSE. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• GPT
• Large Language Models
• Model-Based Systems Engineering
• SysML v2
• Zirui Li

• paper
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →