@inproceedings{tufa-etal-2025-firc,
    title = "{F}i{RC}-{NLP} at {S}em{E}val-2025 Task 11: To Prompt or to Fine-Tune? Approaches for Multilingual Emotion Classification",
    author = "Tufa, Wondimagegnhue  and
      Hassan, Fadi  and
      Migaev, Evgenii  and
      Fu, Yalei",
    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/corrections-2025-08/2025.semeval-1.204/",
    pages = "1549--1556",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper, we describe our system devel-oped for participation in SemEval-2025 Task11: Bridging the Gap in Text-Based EmotionDetection. We compare three approaches formultilingual, multi-label emotion classification:XLM-R, an ensemble of models (XLM-5), anda prompt-based approach. We evaluate the per-formance of these models across a diverse setof languages, ranging from high-resource tolow-resource languages"
}