@inproceedings{chen-etal-2025-drafts,
    title = "Between the Drafts: An Evaluation Framework for Identifying Quality Improvement and Stylistic Differences in Scientific Texts",
    author = "Chen, Danqing  and
      Weber, Ingo  and
      Dietrich, Felix",
    editor = "Akter, Mousumi  and
      Chowdhury, Tahiya  and
      Eger, Steffen  and
      Leiter, Christoph  and
      Opitz, Juri  and
      {\c{C}}ano, Erion",
    booktitle = "Proceedings of the 5th Workshop on Evaluation and Comparison of NLP Systems",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.eval4nlp-1.6/",
    pages = "66--84",
    ISBN = "979-8-89176-305-0",
    abstract = "This study explores the potential of a lightweight, open-source Large Language Model (LLM), demonstrating how its integration with Retrieval-Augmented Generation (RAG) can support cost-effective evaluation of revision quality and writing style differentiation. By retrieving reference documents from a carefully chosen and constructed corpus of peer-reviewed conference proceedings, our framework leverages few-shot in-context learning to track manuscript revisions and venue-specific writing styles. We demonstrate that the LLM-based evaluation aligns closely with human revision histories{---}consistently recognizing quality improvements across revision stages and distinguishing writing styles associated with different conference venues. These findings highlight how a carefully designed evaluation framework, integrated with adequate, representative data, can advance automated assessment of scientific writing."
}