@inproceedings{zemchyk-2026-razreshili,
    title = "Razreshili at {A}rch{EHR}-{QA} 2026: Evidence Alignment via {LLM} Prompting and Cross-Encoder Fine-tuning",
    author = "Zemchyk, Arina",
    editor = "Gupta, Deepak  and
      Thompson, Paul  and
      Ananiadou, Sophia  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.cl4health-1.49/",
    doi = "10.63317/5mop8iu8k9ej",
    pages = "524--529",
    abstract = "We describe our system for Subtask 4 (Evidence Alignment) of the ArchEHR-QA 2026 shared task, which requires aligning each sentence of a clinician-authored answer to the supporting sentence(s) in a clinical note excerpt derived from MIMIC. The task is challenging due to many-to-many alignment structure, answer sentences with no note support, and the semantic gap between clinical note language and answer paraphrases. We explore two approaches: few-shot chain-of-thought prompting with Qwen2.5-7B-Instruct and LoRA fine-tuning of a cross-encoder with combined InfoNCE and BCE loss. Our best system achieves a micro F1 of 67.93 on the test set."
}