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New benchmark tests LLMs on generating coherent SysML diagrams

Researchers have developed SEMAADB, a new dataset and benchmark designed to evaluate the ability of large language models to generate coherent sets of SysML diagrams. The dataset comprises 3,000 engineering contexts, each with five interconnected SysML views, and includes a human-verified benchmark test set of 100 contexts. Evaluations on three language models revealed that while syntax repair is largely solved, semantic repair and maintaining consistency across multiple diagrams remain significant challenges for current models. AI

IMPACT This research highlights current limitations in LLM's ability to generate semantically consistent and coherent multi-diagram systems, indicating areas for future development.

RANK_REASON The cluster contains a research paper detailing a new dataset and benchmark for evaluating LLM capabilities in SysML diagram generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New benchmark tests LLMs on generating coherent SysML diagrams

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The cluster contains a research paper detailing a new dataset and benchmark for evaluating LLM capabilities in SysML diagram generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ardalan Aryashad, Yan Jin ·

    A Validated Dataset and Benchmark for Coherent Multi-Diagram SysML Models

    arXiv:2610.07356v1 Announce Type: cross Abstract: Systems engineers use several diagrams to describe the structure and behavior of systems. Engineers create these diagrams together to make sure that they use the same elements and remain consistent with one another. Large language…