Prompt Mining for Language-based Human Mobility Forecasting
Hao Xue Tianye Tang Ali Payani Flora D. Salim
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.