@inproceedings{wongso-etal-2025-lazarus,
    title = "Lazarus {NLP} at {S}em{E}val-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing",
    author = "Wongso, Wilson  and
      Setiawan, David  and
      Joyoadikusumo, Ananto  and
      Limcorn, Steven",
    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-1.104/",
    pages = "763--772",
    ISBN = "979-8-89176-273-2",
    abstract = "Multi-label emotion classification in low-resource languages remains challenging due to limited annotated data and model adaptability. To address this, we fine-tune large language models (LLMs) using a sentence-label pairing approach, optimizing efficiency while improving classification performance. Evaluating on Sundanese, Indonesian, and Javanese, our method outperforms conventional classifier-based fine-tuning and achieves strong zero-shot cross-lingual transfer. Notably, our approach ranks first in the Sundanese subset of SemEval-2025 Task 11 Track A. Our findings demonstrate the effectiveness of LLM fine-tuning for low-resource emotion classification, underscoring the importance of tailoring adaptation strategies to specific language families in multilingual contexts."
}