@inproceedings{barfi-etal-2025-unclelm,
    title = "{U}ncle{LM} at {S}em{E}val-2025 Task 11: {RAG}-Based Few-Shot Learning and Fine-Tuned Encoders for Multilingual Emotion Detection",
    author = "Barfi, Mobin  and
      Mehrpeyma, Sajjad  and
      Mozayani, Nasser",
    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/transition-to-people-yaml/2025.semeval-1.206/",
    pages = "1563--1569",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our approach for SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. We investigate multiple methodologies, including fine-tuning transformer models and few-shot learning with GPT-4o-mini, incorporating Retrieval-Augmented Generation (RAG) for emotion intensity estimation. Our approach also leverages back-translation for data augmentation and threshold optimization to improve multi-label emotion classification. The experiments evaluate performance across multiple languages, including low-resource settings, with a focus on enhancing cross-lingual emotion detection."
}