@inproceedings{pado-2026-hft,
    title = "{HFT} at {BEA} 2026 Shared Task 2: Blunt-Edge Models for Hybrid Grading",
    author = "Pado, Ulrike",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.86/",
    pages = "1193--1200",
    ISBN = "979-8-89176-409-5",
    abstract = "Open-source LLMs with simple, zero-shot prompts are at best middling graders on the BEA 2026 Automated Grading Shared Task {--} blunt-edge models, in fact. However, they are good enough to support human graders and save them time. We demonstrate the application of a hybrid grading approach that first transparently defines the success criteria and then pairs a zero-shot LLM grader with human review. The hybrid approach outperforms the LLM grader on its own and has the added advantage of keeping the human in the loop."
}