Summary of Urbangpt: Spatio-temporal Large Language Models, by Zhonghang Li et al.
UrbanGPT: Spatio-Temporal Large Language Models
by Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, Chao Huang
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 The paper aims to develop a novel approach for spatio-temporal prediction in urban environments using large language models (LLMs). The goal is to create a model that can accurately forecast and gain insights into the dynamics of urban life, including transportation, population movement, and crime rates. To achieve this, the authors design an UrbanGPT architecture that integrates a spatio-temporal dependency encoder with the instruction-tuning paradigm. This approach enables LLMs to comprehend complex inter-dependencies across time and space, facilitating more comprehensive and accurate predictions under data scarcity. The results demonstrate that the proposed model outperforms state-of-the-art baselines on various public datasets covering different spatio-temporal prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make better predictions about what’s happening in cities over time and space. This is important because we often don’t have enough labeled data (like pictures or notes) to train machines to make good guesses. The authors take inspiration from language models that are really good at understanding text, and create a new model called UrbanGPT. It combines two ideas: one that helps machines understand how things change over time and space, and another that lets them learn from instructions (like “predict this”). They test their model on different public datasets and show that it’s better than other models in making predictions when we don’t have a lot of labeled data. |
Keywords
* Artificial intelligence * Encoder * Instruction tuning
