Summary of Large Language Model Empowered Participatory Urban Planning, by Zhilun Zhou et al.
Large language model empowered participatory urban planning
by Zhilun Zhou, Yuming Lin, Yong Li
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to participatory urban planning that integrates Large Language Models (LLMs) with stakeholders. The framework uses role-play, collaborative generation, and feedback iteration to solve community-level land-use tasks catering to diverse interests. Empirical experiments demonstrate the adaptability and effectiveness of LLMs in various scenarios, surpassing human experts in satisfaction and inclusion. The results also rival state-of-the-art reinforcement learning methods in service and ecology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way for people to work together on city planning using language models. It’s like playing a game where everyone gets to contribute and make decisions. The model helps make sure everyone’s voice is heard and that the plan is fair and good for the community. This approach has been tested in different cities and shows promising results, including making people happy with the outcome. |
Keywords
» Artificial intelligence » Reinforcement learning
