@inproceedings{ulli-mondal-2025-medqwen,
    title = "{M}ed{Q}wen-{PE}: Medical Qwen for Parameter-Efficient Multilingual Patient-Centric Summarization, Question Answering and Information Extraction",
    author = "Ulli, Vinay Babu  and
      Mondal, Anindita",
    editor = "Krishnamurthy, Parameswari  and
      Mujadia, Vandan  and
      Misra Sharma, Dipti  and
      Mary Thomas, Hannah",
    booktitle = "NLP-AI4Health",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.nlpai4health-main.9/",
    pages = "86--92",
    ISBN = "979-8-89176-315-9",
    abstract = "This study addresses the Shared Task on Patient-Centric Multilingual Question Answering, which focuses on generating summaries and patient-oriented answers from multi-turn medical dialogues related to Head and Neck Cancer and Cystic Fibrosis across ten languages. The Qwen3-1.7B model is fine-tuned using QLoRA for three tasks{---}Summarization, Question Answering, and Information Extraction{---}while updating only approximately 1.6{\%} of parameters through task-specific adapter layers. The resulting system demonstrates strong semantic fidelity, as evidenced by high BERTScore and COMET scores, particularly for Kannada, English, Telugu, and Tamil, with comparatively lower performance in Assamese, Bangla, Gujarati, and Marathi. The modular fine-tuning design enables efficient task adaptation while satisfying the constraints on model size and computational resources."
}