Large Language Models are Zero-Shot Next Location Predictors
CoRR(2024)
摘要
Predicting the locations an individual will visit in the future is crucial
for solving many societal issues like disease diffusion and reduction of
pollution among many others. The models designed to tackle next-location
prediction, however, require a significant amount of individual-level
information to be trained effectively. Such data may be scarce or even
unavailable in some geographic regions or peculiar scenarios (e.g., cold-start
in recommendation systems). Moreover, the design of a next-location predictor
able to generalize or geographically transfer knowledge is still an open
research challenge. Recent advances in natural language processing have led to
a rapid diffusion of Large Language Models (LLMs) which have shown good
generalization and reasoning capabilities. These insights, coupled with the
recent findings that LLMs are rich in geographical knowledge, allowed us to
believe that these models can act as zero-shot next-location predictors. This
paper evaluates the capabilities of many popular LLMs in this role,
specifically Llama, GPT-3.5 and Mistral 7B. After designing a proper prompt, we
tested the models on three real-world mobility datasets. The results show that
LLMs can obtain accuracies up to 32.4
over 600
human mobility. Moreover, we show that other LLMs are unable to perform the
task properly. To prevent positively biased results, we also propose a
framework inspired by other studies to test data contamination. Finally, we
explored the possibility of using LLMs as text-based explainers for
next-location prediction showing that can effectively provide an explanation
for their decision. Notably, 7B models provide more generic, but still
reliable, explanations compared to larger counterparts. Code:
github.com/ssai-trento/LLM-zero-shot-NL
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