What’s In A Name?
Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD Files
This work investigates the value in the natural language part and document names users provide when they create CAD models. In a first step towards multi-modal text-CAD learning, our results show that Large Language Models are able to leverage the noisy text data to predict part-part and part-whole relationships, with direct applications in automations and recommendations for part re-use, auto-complete, assembly categorizations, smart tool suggestions and library part recommendations.
Published in the Journal of Computing and Information Science in Engineering.View publication
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