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 this https URL

CityGPT 是一种用于增强大型语言模型在理解城市空间和解决相关城市任务方面能力的系统框架，通过在模型中构建城市规模的世界模型，并使用包含城市知识的多样化指令调整数据集 CityInstruction 来进行模型微调，实现对城市空间的理解和空间推理能力的有效增强。

CityGPT：大型语言模型赋能城市空间认知

CityGPT: Empowering Urban Spatial Cognition of Large Language Models

Crime has become a major concern in many cities, which calls for the rising
demand for timely predicting citywide crime occurrence. Accurate crime
prediction results are vital for the beforehand decision-making of government
to alleviate the increasing concern about the public safety. While many efforts
have been devoted to proposing various spatial-temporal forecasting techniques
to explore dependence across locations and time periods, most of them follow a
supervised learning manner, which limits their spatial-temporal representation
ability on sparse crime data. Inspired by the recent success in self-supervised
learning, this work proposes a Spatial-Temporal Hypergraph Self-Supervised
Learning framework (ST-HSL) to tackle the label scarcity issue in crime
prediction. Specifically, we propose the cross-region hypergraph structure
learning to encode region-wise crime dependency under the entire urban space.
Furthermore, we design the dual-stage self-supervised learning paradigm, to not
only jointly capture local- and global-level spatial-temporal crime patterns,
but also supplement the sparse crime representation by augmenting region
self-discrimination. We perform extensive experiments on two real-life crime
datasets. Evaluation results show that our ST-HSL significantly outperforms
state-of-the-art baselines. Further analysis provides insights into the
superiority of our ST-HSL method in the representation of spatial-temporal
crime patterns. The implementation code is available at
this https URL.

提出了一种基于超图自监督学习的空间 - 时间犯罪预测框架，用于解决标签稀缺问题，并在两个真实犯罪数据集上得到了显着优于现有基线的评估结果。