Large Language Models are Zero-Shot Next Location Predictors
<|reference_start|>Large Language Models are Zero-Shot Next Location Predictors: Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution. However, next-location predictors require a significant amount of individual-level information that may be scarce or unavailable in some scenarios (e.g., cold-start). Large Language Models (LLMs) have shown good generalization and reasoning capabilities and are rich in geographical knowledge, allowing us to believe that these models can act as zero-shot next-location predictors. We tested more than 15 LLMs on three real-world mobility datasets and we found that LLMs can obtain accuracies up to 36.2%, a significant relative improvement of almost 640% when compared to other models specifically designed for human mobility. We also test for data contamination and explored the possibility of using LLMs as text-based explainers for next-location prediction, showing that, regardless of the model size, LLMs can explain their decision.<|reference_end|>
@article{beneduce2024large,
    title={Large Language Models are Zero-Shot Next Location Predictors},
    author={Ciro Beneduce, Bruno Lepri, Massimiliano Luca},
    journal={arXiv preprint arXiv:2405.20962},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20962},
    primaryClass={cs.CY cs.AI cs.CL}
}