@inproceedings{morozov-etal-2025-empaths,
    title = "Empaths at {S}em{E}val-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction",
    author = "Morozov, Lev  and
      Mogilevskii, Aleksandr  and
      Shirnin, Alexander",
    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-1.259/",
    pages = "2000--2007",
    ISBN = "979-8-89176-273-2",
    abstract = "The paper introduces EmoRAG, a retrieval-augmented emotion detection system designed for the SemEval-2025 Task 11. It uses an ensemble of models, retrieving similar examples to prompt large language models (LLMs) for emotion predictions. The retriever component fetches the most relevant examples from a database, which are then used as few-shot prompts for the models. EmoRAG achieves strong, scalable performance across languages with no training at all, demonstrating effectiveness in both high and low-resource languages."
}