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BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement

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arxiv 2412.14203 v1 pith:CX27VG53 submitted 2024-12-16 cs.HC cs.AI

Yuhao Du , Shunian Chen , Wenbo Zan , Peizhao Li , Mingxuan Wang , Dingjie Song , Bo Li , Yan Hu
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Benyou Wang
This is my paper · ORCID
classification cs.HCcs.AI
keywords modelsblenderllmllmsself-improvementtrainingapplicationcomputer-aideddataset
verification ladder T0 review T1 audit T2 compute T3 formal
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The application of Large Language Models (LLMs) in Computer-Aided Design (CAD) remains an underexplored area, despite their remarkable advancements in other domains. In this paper, we present BlenderLLM, a novel framework for training LLMs specifically for CAD tasks leveraging a self-improvement methodology. To support this, we developed a bespoke training dataset, BlendNet, and introduced a comprehensive evaluation suite, CADBench. Our results reveal that existing models demonstrate significant limitations in generating accurate CAD scripts. However, through minimal instruction-based fine-tuning and iterative self-improvement, BlenderLLM significantly surpasses these models in both functionality and accuracy of CAD script generation. This research establishes a strong foundation for the application of LLMs in CAD while demonstrating the transformative potential of self-improving models in advancing CAD automation. We encourage further exploration and adoption of these methodologies to drive innovation in the field. The dataset, model, benchmark, and source code are publicly available at https://github.com/FreedomIntelligence/BlenderLLM

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