With the advancement of large language models, language-based forecasting has recently emerged as an innovative approach for predicting human mobility patterns. The core idea is to use prompts to transform the raw mobility data given as numerical values into natural language sentences so that the language models can be leveraged to generate the description for future observations. However, previous studies have only employed fixed and manually designed templates to transform numerical values into sentences. Since the forecasting performance of language models heavily relies on prompts, using fixed templates for prompting may limit the forecasting capability of language models. In this paper, we propose a novel framework for prompt mining in language-based mobility forecasting, aiming to explore diverse prompt design strategies. Specifically, the framework includes a prompt generation stage based on the information entropy of prompts and a prompt refinement stage to integrate mechanisms such as the chain of thought. Experimental results on real-world large-scale data demonstrate the superiority of generated prompts from our prompt mining pipeline. Additionally, the comparison of different prompt variants shows that the proposed prompt refinement process is effective. Our study presents a promising direction for further advancing language-based mobility forecasting.

翻译：随着大语言模型的发展，基于语言的预测近期成为一种创新方法，用于预测人类流动性模式。其核心思想是通过提示（prompts）将原始数值型流动性数据转化为自然语言句子，从而利用语言模型生成对未来观测的描述。然而，以往研究仅采用固定且人工设计的模板将数值转化为句子。由于语言模型的预测性能高度依赖于提示，使用固定模板进行提示可能限制语言模型的预测能力。本文提出一种新颖的语言流动性预测提示挖掘框架，旨在探索多样化的提示设计策略。具体而言，该框架包括基于提示信息熵的提示生成阶段，以及集成思维链等机制的提示精炼阶段。在真实大规模数据集上的实验结果表明，我们提出的提示挖掘流程生成的提示具有优越性。此外，不同提示变体的比较显示，所提出的提示精炼过程行之有效。本研究为进一步推进基于语言的流动性预测提供了有前景的方向。