@inproceedings{xu-etal-2025-nycu,
    title = "{NYCU}-{NLP} at {S}em{E}val-2025 Task 11: Assembling Small Language Models for Multilabel Emotion Detection and Intensity Prediction",
    author = "Xu, Zhe - Yu  and
      Wu, Yu - Hsin  and
      Lee, Lung - Hao",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.semeval-1.149/",
    pages = "1129--1135",
    ISBN = "979-8-89176-273-2",
    abstract = "This study describes the design of the NYCU-NLP system for the SemEval-2025 Task 11 that focuses on multi-lingual text-based emotion analysis. We instruction-tuned three small language models: Gemma-2 (27B), Mistral-small-3 (22B), and Phi-4 (14B) and then assembled them as our main system architecture. Our NYCU-NLP system participated the English Track A for multilabel emotion detection and English Track B for emotion intensity prediction. Experimental results show our best-performing submission produced a macro-averaging F1 score of 0.8225, ranking second of 90 participating teams for Track A, and ranked second among 41 teams for Track B with a Pearson correlation coefficient of 0.8373."
}