@inproceedings{karas-spiewak-2025-opi,
    title = "{OPI}-{DRO}-{HEL} at at {S}em{E}val-2025 Task 11: Few-shot prompting for Text-based Emotion Recognition",
    author = "Kara{\'s}, Daniel  and
      {\'S}piewak, Martyna",
    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-1.164/",
    pages = "1233--1240",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our system, developed as our contribution to SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection task, in particular track A, Multi-label Emotion Detection subtask. Our approach relies on two distinct components: semantic search for top N most similar inputs from training set and an interface to pretrained LLM being prompted using the found examples. We examine several prompting strategies and their impact on overall performance of the proposed solution."
}