Summary of Citybench: Evaluating the Capabilities Of Large Language Models For Urban Tasks, by Jie Feng et al.
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
by Jie Feng, Jun Zhang, Tianhui Liu, Xin Zhang, Tianjian Ouyang, Junbo Yan, Yuwei Du, Siqi Guo, Yong Li
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 presents a systematic and reliable evaluation platform, CityBench, designed to assess the capabilities of large language models (LLMs) and vision-language models (VLMs) for various tasks in urban research. The platform integrates diverse urban data and simulates fine-grained urban dynamics through CityData and CitySimu, respectively. Eight representative urban tasks are categorized into perception-understanding and decision-making, and the performance of 30 well-known LLMs and VLMs is evaluated across 13 cities worldwide. The results show that advanced LLMs and VLMs excel in tasks requiring commonsense and semantic understanding abilities, such as understanding human dynamics and inferring urban images. However, they struggle with challenging tasks demanding professional knowledge and high-level reasoning abilities, like geospatial prediction and traffic control. This study provides valuable insights for utilizing and developing LLMs in the future. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool called CityBench to test how well computers can understand urban things. It’s like a big test that checks if AI models are good at doing tasks like understanding pictures of cities, understanding how people move around, and making predictions about traffic. The researchers used many different computer models and tested them on 13 different cities. They found out that some models are really good at understanding simple urban things, but struggle with more complicated problems. This information will help make AI better for city-related tasks. |
