@inproceedings{creanga-etal-2025-team-unibuc,
    title = "Team {U}nibuc - {NLP} at {S}em{E}val-2025 Task 11: Few-shot text-based emotion detection",
    author = "Creanga, Claudiu  and
      Marchitan, Teodor - George  and
      Dinu, Liviu",
    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-1.65/",
    pages = "468--475",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. Withour final system, for the multi-label emotion detection track (track A), we got an F1-macro of 0.7546 (26/96 teams) for the English subset, 0.1727 (35/36 teams) for the Portuguese (Mozambican) subset and 0.325 (1/31 teams) for the Emakhuwa subset."
}