@inproceedings{lin-etal-2024-nycu,
    title = "{NYCU}-{NLP} at {EXALT} 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger Detection",
    author = "Lin, Tzu-Mi  and
      Xu, Zhe-Yu  and
      Zhou, Jian-Yu  and
      Lee, Lung-Hao",
    editor = "De Clercq, Orph{\'e}e  and
      Barriere, Valentin  and
      Barnes, Jeremy  and
      Klinger, Roman  and
      Sedoc, Jo{\~a}o  and
      Tafreshi, Shabnam",
    booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.wassa-1.50/",
    doi = "10.18653/v1/2024.wassa-1.50",
    pages = "505--510",
    abstract = "This study describes the model design of the NYCU-NLP system for the EXALT shared task at the WASSA 2024 workshop. We instruction-tune several large language models and then assemble various model combinations as our main system architecture for cross-lingual emotion and trigger detection in tweets. Experimental results showed that our best performing submission is an assembly of the Starling (7B) and Llama 3 (8B) models. Our submission was ranked sixth of 17 participating systems for the emotion detection subtask, and fifth of 7 systems for the binary trigger detection subtask."
}