التفاصيل البيبلوغرافية
| العنوان: |
Large Language Models are Zero-Shot Next Location Predictors |
| المؤلفون: |
Beneduce, Ciro, Lepri, Bruno, Luca, Massimiliano |
| سنة النشر: |
2024 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computation and Language |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2405.20962 |
| رقم الانضمام: |
edsarx.2405.20962 |
| قاعدة البيانات: |
arXiv |