@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",
    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.",
}

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.
Xiao, Zhaomin Huang, Yan Blanco, Eduardo
Analyzing Large Language Models' Capability in Location Prediction