@inproceedings{zhao-vydiswaran-2025-team,
    title = "Team {NLP}4{H}ealth at {C}hemo{T}imelines 2025: Finetuning Large Language Models for Temporal Relation Extractions from Clinical Notes",
    author = "Zhao, Zhe  and
      Vydiswaran, V.G.Vinod",
    editor = "Ben Abacha, Asma  and
      Bethard, Steven  and
      Bitterman, Danielle  and
      Naumann, Tristan  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
    month = oct,
    year = "2025",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-brigap/2025.clinicalnlp-1.4/",
    pages = "22--29",
    abstract = "Extracting chemotherapy timelines from clinical narratives is a challenging task, but critical for cancer research and practice. In this paper, we present our approach and the research investigation we conducted to participate in Subtask 1 of the ChemoTimelines 2025 shared task on predicting temporal relations between pre-identified events and time expressions. We evaluated multiple fine-tuned large language models for the task. We used supervised fine-tuning strategies for Llama3-8B model to classify temporal relations. Further, we set up zero-shot prompting for Qwen3-14B to normalize time expressions. We also pre-trained and fine-tuned a Llama3-3B model using unlabeled notes and achieved results comparable with the fine-tuned Llama3-8B model. Our results demonstrate the effectiveness of fine-tuning and continual pre-training strategies in adapting large language models to domain-specific tasks."
}