Zhaomin Xiao


2024

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Analyzing Large Language Models’ Capability in Location Prediction
Zhaomin Xiao | Yan Huang | Eduardo Blanco
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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.

2023

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Context Helps Determine Spatial Knowledge from Tweets
Zhaomin Xiao | Yan Huang | Eduardo Blanco
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

2022

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Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach
Zhaomin Xiao | Eduardo Blanco
Proceedings of the 29th International Conference on Computational Linguistics

This paper introduces the problem of determining whether people are located in the places they mention in their tweets. In particular, we investigate the role of text and images to solve this challenging problem. We present a new corpus of tweets that contain both text and images. Our analyses show that this problem is multimodal at its core: human judgments depend on whether annotators have access to the text, the image, or both. Experimental results show that a neural architecture that combines both modalities yields better results. We also conduct an error analysis to provide insights into why and when each modality is beneficial.