@inproceedings{saeedi-etal-2025-gt,
    title = "{GT}-{NLP} at {S}em{E}val-2025 Task 11: {E}mo{R}ationale, Evidence-Based Emotion Detection via Retrieval-Augmented Generation",
    author = "Saeedi, Daniel  and
      Kheirandish, Alireza  and
      Saeedi, Sirwe  and
      Sahour, Hossein  and
      Panahi, Aliakbar  and
      Naeeni, Iman",
    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-1.90/",
    pages = "640--650",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection in multilingual settings presents significant challenges, particularly for low-resource languages where labeled datasets are scarce. To address these limitations, we introduce EmoRationale, a Retrieval-Augmented Generation (RAG) framework designed to enhance explainability and cross-lingual generalization in emotion detection. Our approach combines vector-based retrieval with in-context learning in large language models (LLMs), using semantically relevant examples to enhance classification accuracy and interpretability. Unlike traditional fine-tuning methods, our system provides evidence-based reasoning for its predictions, making emotion detection more transparent and adaptable across diverse linguistic contexts. Experimental results on the SemEval-2025 Task 11 dataset demonstrate that our RAG-based method achieves strong performance in multi-label emotion classification, emotion intensity assessment, and cross-lingual emotion transfer, surpassing conventional models in interpretability while remaining cost-effective."
}