{
  "id": 7123,
  "url": "https://arxiv.org/abs/2603.16357v2",
  "title": "Beyond Grading Accuracy: Exploring Alignment of TAs and LLMs",
  "summary": "In this paper, we investigate the potential of open-source Large Language Models (LLMs) for grading Unified Modeling Language (UML) class diagrams. In contrast to existing work, which primarily evaluates proprietary LLMs, we focus on non-proprietary models, making our approach suitable for universities where transparency and cost are critical. Additionally, existing studies assess performance over complete diagrams rather than individual criteria, offering limited insight into how automated grad",
  "authors": "Matthijs Jansen op de Haar, Nacir Bouali, Faizan Ahmed",
  "category": "research",
  "topics": "safety-alignment,transparency,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-17T10:40:35.000Z",
  "fetched_at": "2026-07-14T16:32:59.166Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7123",
  "original_url": "https://arxiv.org/abs/2603.16357v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}