Analyzing Large Language Models’ Capability in Location Prediction

Zhaomin Xiao; Yan Huang; Eduardo Blanco

COLING 2024main21 citations

Analyzing Large Language Models’ Capability in Location Prediction

Abstract

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.

BibTeX

@inproceedings{xiao-etal-2024-analyzing,
    title = "Analyzing Large Language Models' Capability in Location Prediction",
    author = "Xiao, Zhaomin  and
      Huang, Yan  and
      Blanco, Eduardo",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.85/",
    pages = "951--958"
}