{
  "id": 6704,
  "url": "https://arxiv.org/abs/2604.14175v1",
  "title": "QU-NLP at ArchEHR-QA 2026: Two-Stage QLoRA Fine-Tuning of Qwen3-4B for Patient-Oriented Clinical Question Answering and Evidence Sentence Alignment",
  "summary": "We present a unified system addressing both Subtask 3 (answer generation) and Subtask 4 (evidence sentence alignment) of the ArchEHR-QA Shared Task. For Subtask 3, we apply two-stage Quantised Low-Rank Adaptation (QLoRA) to Qwen3-4B loaded in 4-bit NF4 quantisation: first on 30,000 samples from the emrQA-MedSQuAD corpus to establish clinical domain competence, then on the 20 annotated development cases to learn the task-specific output style. Our system achieves an overall score of 32.87 on the ",
  "authors": "Mohammad AL-Smadi",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-26T11:24:13.000Z",
  "fetched_at": "2026-07-14T16:32:41.666Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6704",
  "original_url": "https://arxiv.org/abs/2604.14175v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}