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%, a significant relative improvement of
over 600% when compared to sophisticated DL models specifically designed for
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

利用大型语言模型作为零样本下一个位置的预测器进行了评估，并展示了其较高的准确性和解释能力，为解决诸如疾病传播和减少污染等社会问题提供了一种新思路。

大型语言模型的零 - shot 下一个位置预测模型

Large Language Models are Zero-Shot Next Location Predictors

Next-location prediction, consisting of forecasting a user's location given
their historical trajectories, has important implications in several fields,
such as urban planning, geo-marketing, and disease spreading. Several
predictors have been proposed in the last few years to address it, including
last-generation ones based on deep learning. This paper tests the
generalization capability of these predictors on public mobility datasets,
stratifying the datasets by whether the trajectories in the test set also
appear fully or partially in the training set. We consistently discover a
severe problem of trajectory overlapping in all analyzed datasets, highlighting
that predictors memorize trajectories while having limited generalization
capacities. We thus propose a methodology to rerank the outputs of the
next-location predictors based on spatial mobility patterns. With these
techniques, we significantly improve the predictors' generalization capability,
with a relative improvement on the accuracy up to 96.15% on the trajectories
that cannot be memorized (i.e., low overlap with the training set).

本文测试了基于深度学习的预测器在公共移动数据集上的泛化能力，发现所有分析数据集中存在严重的轨迹重叠问题，表明预测器能够记忆轨迹但泛化能力有限。因此，本文提出了一种基于空间移动模式重新排名的方法，在不能记忆的轨迹上提高了预测器的准确性相对改进高达 96.15％。