PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916142207991808 |
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| author | Zhu, He Zhang, Wenjia Huang, Nuoxian Li, Boyang Niu, Luyao Fan, Zipei Lun, Tianle Tao, Yicheng Su, Junyou Gong, Zhaoya Fang, Chenyu Liu, Xing |
| author_facet | Zhu, He Zhang, Wenjia Huang, Nuoxian Li, Boyang Niu, Luyao Fan, Zipei Lun, Tianle Tao, Yicheng Su, Junyou Gong, Zhaoya Fang, Chenyu Liu, Xing |
| contents | In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized Large Language Model tailored for urban and spatial planning. Developed through collaborative efforts with institutions like the Chinese Academy of Urban Planning, PlanGPT leverages a customized local database retrieval framework, domain-specific fine-tuning of base models, and advanced tooling capabilities. Empirical tests demonstrate that PlanGPT has achieved advanced performance, delivering responses of superior quality precisely tailored to the intricacies of urban planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19273 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval Zhu, He Zhang, Wenjia Huang, Nuoxian Li, Boyang Niu, Luyao Fan, Zipei Lun, Tianle Tao, Yicheng Su, Junyou Gong, Zhaoya Fang, Chenyu Liu, Xing Computation and Language In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized Large Language Model tailored for urban and spatial planning. Developed through collaborative efforts with institutions like the Chinese Academy of Urban Planning, PlanGPT leverages a customized local database retrieval framework, domain-specific fine-tuning of base models, and advanced tooling capabilities. Empirical tests demonstrate that PlanGPT has achieved advanced performance, delivering responses of superior quality precisely tailored to the intricacies of urban planning. |
| title | PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.19273 |