@inproceedings{liao-etal-2025-automated,
title = "Automated {CAD} Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models",
author = "Liao, JianXing and
Xu, Junyan and
Sun, Yatao and
Tang, Maowen and
He, Sicheng and
Liao, Jingxian and
Yu, Shui and
Li, Yun and
Guan, Xiaohong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1054/",
doi = "10.18653/v1/2025.acl-long.1054",
pages = "21720--21748",
ISBN = "979-8-89176-251-0",
abstract = "Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts.The code is available at https://jianxliao.github.io/cadllm-page/"
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<abstract>Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts.The code is available at https://jianxliao.github.io/cadllm-page/</abstract>
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%0 Conference Proceedings
%T Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models
%A Liao, JianXing
%A Xu, Junyan
%A Sun, Yatao
%A Tang, Maowen
%A He, Sicheng
%A Liao, Jingxian
%A Yu, Shui
%A Li, Yun
%A Guan, Xiaohong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liao-etal-2025-automated
%X Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts.The code is available at https://jianxliao.github.io/cadllm-page/
%R 10.18653/v1/2025.acl-long.1054
%U https://aclanthology.org/2025.acl-long.1054/
%U https://doi.org/10.18653/v1/2025.acl-long.1054
%P 21720-21748
Markdown (Informal)
[Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models](https://aclanthology.org/2025.acl-long.1054/) (Liao et al., ACL 2025)
ACL
- JianXing Liao, Junyan Xu, Yatao Sun, Maowen Tang, Sicheng He, Jingxian Liao, Shui Yu, Yun Li, and Xiaohong Guan. 2025. Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21720–21748, Vienna, Austria. Association for Computational Linguistics.