@inproceedings{chen-etal-2025-csiro,
    title = "{CSIRO}-{LT} at {S}em{E}val-2025 Task 11: Adapting {LLM}s for Emotion Recognition for Multiple Languages",
    author = {Chen, Jiyu  and
      B{\"o}l{\"u}c{\"u}, Necva  and
      Karimi, Sarvnaz  and
      Molla, Diego  and
      Paris, Cecile},
    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-1.48/",
    pages = "336--342",
    ISBN = "979-8-89176-273-2",
    abstract = "Detecting emotions across different languages is challenging due to the varied and culturally nuanced ways of emotional expressions. The Semeval 2025 Task 11: Bridging the Gap in Text-Based emotion shared task was organised to investigate emotion recognition across different languages. The goal of the task is to implement an emotion recogniser that can identify the basic emotional states that general third-party observers would attribute to an author based on their written text snippet, along with the intensity of those emotions. We report our investigation of various task-adaptation strategies for LLMs in emotion recognition. We show that the most effective method for this task is to fine-tune a pre-trained multilingual LLM for each language."
}