Large Language Models are Zero-Shot Next Location Predictors

• URL: http://arxiv.org/abs/2405.20962v3
• Date: Fri, 23 Aug 2024 09:24:22 GMT
• Title: Large Language Models are Zero-Shot Next Location Predictors
• Authors: Ciro Beneduce, Bruno Lepri, Massimiliano Luca,
• Abstract summary: Large Language Models (LLMs) have shown good generalization and reasoning capabilities. LLMs can obtain accuracies up to 36.2%, a significant improvement of almost 640% when compared to other models specifically designed for human mobility.
• Score: 4.315451628809687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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