Enterprise AI Analysis
CityGPT: Empowering Urban Spatial Cognition of Large Language Models
This paper introduces CityGPT, a systematic framework to enhance Large Language Models (LLMs) with urban spatial cognition capabilities. It includes CityInstruction for dataset creation, Self-Weighted Fine-Tuning (SWFT) for training, and CityEval for comprehensive evaluation.
Executive Impact & Key Metrics
CityGPT significantly boosts LLM performance in urban spatial tasks, offering tangible improvements for enterprise applications.
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Spatial Reasoning Gain
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Max Spatial Reasoning Gain
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Urban Semantics Gain
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Introduction & Motivation
Large language models (LLMs) have achieved notable success but struggle with real-life geospatial tasks due to a lack of physical world knowledge. CityGPT aims to bridge this gap by integrating a city-scale 'world model' to enhance LLMs' understanding of urban space and improve their ability to solve related tasks. The current limitation of LLMs in urban scenarios, particularly at the city scale, highlights the need for a systematic framework to enhance spatial cognition, which this paper addresses.
CityInstruction Dataset
CityInstruction is a diverse instruction tuning dataset constructed to inject urban knowledge into LLMs. It simulates human mobility behaviors and spatial perceptions through a mobility simulator with real map data. This dataset comprises:
- CityQA: Simple questions about single urban entities (PoI, AoI, road, junction) covering basic information and nearby relations.
- CityWalk: Multi-step exploration simulating long-term temporal and spatial urban experiences.
- CityReasoning: Problems requiring explicit intermediary spatial reasoning steps for high-level urban tasks, designed to align with human cognitive habits.
SWFT Method
The Self-Weighted Fine-Tuning (SWFT) method is proposed to robustly enhance LLMs' spatial skills while minimizing negative impact on general performance. It addresses the issue of 'low-quality' data points by assigning smaller learning weights based on evaluation losses from a base LLM and a warm-up LLM. This ensures that the model effectively focuses on high-quality data, leading to improved and more robust knowledge learning and performance, as demonstrated by improved CityEval and general benchmark scores.
CityEval Benchmark
CityEval is a comprehensive evaluation benchmark designed to assess LLMs' capabilities in various urban scenarios. It is divided into four task groups:
- City Image: Measures intuitive understanding of urban fundamental elements (paths, edges, districts, nodes, landmarks).
- Urban Semantics: Evaluates understanding of human activities and urban functions based on POI distribution.
- Spatial Reasoning: Assesses quantitative reasoning and spatial cognition with and without context.
- Composite Tasks: Evaluates integrated capabilities for realistic urban tasks like mobility prediction, trajectory generation, and spatial navigation.
54.50%
City Image Performance Increase (Beijing)
Enterprise Process Flow
General LLMs
→
CityInstruction
→
Training (SWFT)
→
Evaluating (CityEval)
→
CityGPT
| Model | City Image (CI)↑ | Urban Semantics (US)↑ | Spatial Reasoning (SR)↑ | MMLU | GSM8K | BBH |
|---|---|---|---|---|---|---|
| Qwen2.5-7B | 0.325 | 0.587 | 0.164 | 74.15 | 80.21 | 66.01 |
| CityGPT-Qwen2.5-7B | 0.502 | 0.620 | 0.552 | 74.72 | 77.18 | 70.03 |
| LLama3-8B | 0.286 | 0.520 | 0.285 | 68.33 | 79.38 | 52.88 |
| CityGPT-LLama3-8B | 0.554 | 0.680 | 0.708 | 56.89 | 60.58 | 53.92 |
Spatial Transferability Across Cities
CityGPT demonstrates significant spatial transferability. Models trained with data from Beijing consistently outperform base models in other cities (London, New York, Paris) on CityEval tasks. For example, CityGPT-Qwen2.5-7B trained on Beijing data achieves superior results in London across all metrics. This indicates that CityGPT learns general spatial cognition knowledge applicable across different urban environments, rather than just location-specific data. The performance in 'without-context' reasoning is generally lower, suggesting room for improvement in handling complex reasoning tasks.
- Models trained in one city (e.g., Beijing) show superior performance in other cities (London, New York, Paris).
- This confirms that CityGPT learns generalizable spatial cognition, not just location-specific knowledge.
- Performance is lower in 'without-context' reasoning, indicating areas for future enhancement.
Calculate Your Potential AI ROI
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Your CityGPT Implementation Roadmap
A clear path to integrate advanced spatial cognition into your LLM infrastructure and urban applications.
Phase 1: Discovery & Data Integration
Initial consultation, data assessment, and integration of CityInstruction dataset tailored to your urban environment and use cases.
Phase 2: Model Fine-Tuning with SWFT
Leverage our Self-Weighted Fine-Tuning (SWFT) method to enhance your LLMs' spatial cognition abilities, ensuring robust performance.
Phase 3: Custom CityEval & Validation
Comprehensive evaluation using a customized CityEval benchmark to validate performance across your specific urban scenarios and tasks.
Phase 4: Deployment & Optimization
Seamless deployment of the fine-tuned CityGPT model into your existing systems, followed by ongoing optimization and support.
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