  Large language models(LLMs) with powerful language generation and reasoning
capabilities have already achieved success in many domains, e.g., math and code
generation. However, due to the lacking of physical world's corpus and
knowledge during training, they usually fail to solve many real-life tasks in
the urban space. In this paper, we propose CityGPT, a systematic framework for
enhancing the capability of LLMs on understanding urban space and solving the
related urban tasks by building a city-scale world model in the model. First,
we construct a diverse instruction tuning dataset CityInstruction for injecting
urban knowledge and enhancing spatial reasoning capability effectively. By
using a mixture of CityInstruction and general instruction data, we fine-tune
various LLMs (e.g., ChatGLM3-6B, Qwen1.5 and LLama3 series) to enhance their
capability without sacrificing general abilities. To further validate the
effectiveness of proposed methods, we construct a comprehensive benchmark
CityEval to evaluate the capability of LLMs on diverse urban scenarios and
problems. Extensive evaluation results demonstrate that small LLMs trained with
CityInstruction can achieve competitive performance with commercial LLMs in the
comprehensive evaluation of CityEval. The source codes are openly accessible to
the research community via https://github.com/tsinghua-fib-lab/CityGPT.
