Analyzing Large Language Models' Capability in Location Prediction

In this paper, we investigate and evaluate large language models' capability in location prediction. We present experimental results with four models-FLAN-T5, FLAN-UL2, FLAN-Alpaca, and ChatGPT-in various instruction finetuning and exemplar settings. We analyze whether taking into account the context-tweets published before and after the tweet mentioning a location-is beneficial. Additionally, we conduct an ablation study to explore whether instruction modification is beneficial. Lastly, our qualitative analysis sheds light on the errors made by the best-performing model.