Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data

The paper demonstrates that fine‑tuning large language models (LLMs) on local tourist trajectory data can predict visitor movements under varying conditions. Using 566 trajectories from Wakayama Castle Park, Japan, the authors fine‑tuned Llama‑3.1‑8B, achieving 49.1% accuracy for next point‑of‑interest predictions and maintaining strong performance even on undersampled scenarios such as rainy days. This shows that LLMs can serve as high‑fidelity, context‑aware behavior models for tourist prediction and enable counterfactual analysis of mobility interventions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• fine-tuning