BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913618145050624 |
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| author | Du, Yuhao Chen, Shunian Zan, Wenbo Li, Peizhao Wang, Mingxuan Song, Dingjie Li, Bo Hu, Yan Wang, Benyou |
| author_facet | Du, Yuhao Chen, Shunian Zan, Wenbo Li, Peizhao Wang, Mingxuan Song, Dingjie Li, Bo Hu, Yan Wang, Benyou |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement Du, Yuhao Chen, Shunian Zan, Wenbo Li, Peizhao Wang, Mingxuan Song, Dingjie Li, Bo Hu, Yan Wang, Benyou Human-Computer Interaction Artificial Intelligence 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 |
| title | BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2412.14203 |