@inproceedings{liu-zhang-2026-sdpa,
    title = "{SDPA} at {BEA} 2026 Shared Task 2: Efficient {LLM} Fine-Tuning for Rubric-based Short Answer Scoring",
    author = "Liu, Zhexiong  and
      Zhang, Jing",
    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-1.92/",
    pages = "1244--1251",
    ISBN = "979-8-89176-409-5",
    abstract = "Automated short-answer scoring (ASA) is an important yet challenging task in educational assessment as it aims to evaluate open-ended student responses against predefined scoring rubrics that are often interrelated. Although large language models (LLMs) have demonstrated impressive capabilities in text understanding and reasoning, their application to ASA has primarily focused on prompt-based inference, largely due to the limited availability of annotated data required for effective model training. In this work, we investigate parameter-efficient fine-tuning strategies for LLMs using ASA annotations in German. Our experiments show that fine-tuned LLMs consistently outperform both prompt-based and ensemble-based language models, suggesting domain-adaptive LLM fine-tuning is more effective than prompting alone for ASA."
}