AI RESEARCH
A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction
arXiv CS.CL
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ArXi:2604.06207v1 Announce Type: new This paper investigates nstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), aiming to accurately forecast a user's subsequent location based on historical check-in data. While in-context learning (ICL) with LLMs has recently gained attention as a promising alternative to traditional supervised approaches, the effectiveness of ICL significantly depends on the selected nstration.