随着城市化与气候变化加剧，城市的脆弱性日益上升，因而亟需在极端事件（如极端天气）期间准确预测人类移动性，以支撑基于位置的早期灾害预警、救援资源预部署等下游任务。然而，现有移动性预测模型主要面向常规场景设计，在极端场景下因人类移动模式发生偏移而难以适应。为解决该问题，我们提出\textbf{X-MLM}——一种面向极端场景的跨极端事件移动性语言模型框架，该框架可嵌入现有深度移动性预测方法中：利用大语言模型（LLM）建模移动意图，并在不同城市间迁移各类极端事件对移动意图影响的共性知识。该框架采用检索增强型意图预测器（RAG-Enhanced Intention Predictor）预测下一意图，再通过基于LLM的意图精炼器（Intention Refiner）对其进行优化，最后由意图调制的位置预测器（Intention-Modulated Location Predictor）将意图映射至具体位置。大量实验表明，X-MLM在Acc@1指标上较基线方法提升32.8%，在预测非移动性（immobility）的F1分数上提升35.0%。代码开源地址：https://github.com/tsinghua-fib-lab/XMLM。

The vulnerability of cities has increased with urbanization and climate change, making it more important to predict human mobility during extreme events (e.g., extreme weather) for downstream tasks including location-based early disaster warning and pre-allocating rescue resources, etc. However, existing human mobility prediction models are mainly designed for normal scenarios, and fail to adapt to extreme scenarios due to the shift of human mobility patterns under extreme scenarios. To address this issue, we introduce \textbf{X-MLM}, a cross-e\textbf{X}treme-event \textbf{M}obility \textbf{L}anguge \textbf{M}odel framework for extreme scenarios that can be integrated into existing deep mobility prediction methods by leveraging LLMs to model the mobility intention and transferring the common knowledge of how different extreme events affect mobility intentions between cities. This framework utilizes a RAG-Enhanced Intention Predictor to forecast the next intention, refines it with an LLM-based Intention Refiner, and then maps the intention to an exact location using an Intention-Modulated Location Predictor. Extensive experiments illustrate that X-MLM can achieve a 32.8\% improvement in terms of Acc@1 and a 35.0\% improvement in terms of the F1-score of predicting immobility compared to the baselines. The code is available at https://github.com/tsinghua-fib-lab/XMLM.