@inproceedings{bai-stede-2026-fine,
    title = "Fine-Grained Content Zone Prediction in {G}erman Argumentative Essays Using {LLM}s",
    author = "Bai, Xiaoyu  and
      Stede, Manfred",
    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-1.27/",
    pages = "405--418",
    ISBN = "979-8-89176-409-5",
    abstract = "We introduce FDE-Arg, a newly compiled dataset of argumentative student essays in German. We use two Llama models of different sizes to label sentence-level content zones both in FDE-Arg and in an existing dataset of source-dependent argumentative essays. We investigate three approaches for improving model performance: a) Incorporating targeted task information into the prompt text; b) few-shot prompting with up to 10 examples selected on the basis of similarity with the target instance; and c) parameter-efficient fine-tuning. We observe that both incorporating additional information in the prompts and similarity-based few-shot prompting have produced highly promising performance gains over the baseline."
}