@inproceedings{xiao-etal-2024-analyzing-large,
title = "Analyzing Large Language Models{'} Capability in Location Prediction",
author = "Xiao, Zhaomin and
Blanco, Eduardo and
Huang, Yan",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Analyzing Large Language Models’ Capability in Location Prediction
%A Xiao, Zhaomin
%A Blanco, Eduardo
%A Huang, Yan
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F xiao-etal-2024-analyzing-large
%X 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.
%U https://aclanthology.org/2024.lrec-main.85
%P 951-958
Markdown (Informal)
[Analyzing Large Language Models’ Capability in Location Prediction](https://aclanthology.org/2024.lrec-main.85) (Xiao et al., LREC-COLING 2024)
ACL