Prompt Mining for Language-based Human Mobility Forecasting

TL;DR

This paper introduces a prompt mining framework that enhances language-based human mobility forecasting by generating and refining prompts, leading to improved prediction performance over fixed templates.

Contribution

It proposes a novel prompt mining pipeline with entropy-based prompt generation and refinement mechanisms, advancing the use of language models for mobility forecasting.

Findings

Generated prompts outperform fixed templates in forecasting accuracy.

Prompt refinement mechanisms significantly improve model performance.

Experimental results validate the effectiveness of the proposed framework.

Abstract

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.