@inproceedings{gu-etal-2026-large,
    title = "Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators",
    author = "Gu, Feng  and
      Li, Zongxia  and
      Colon, Carlos R.  and
      Evans, Benjamin  and
      Mondal, Ishani  and
      Boyd-Graber, Jordan Lee",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.4/",
    pages = "71--89",
    ISBN = "979-8-89176-395-1",
    abstract = "Event annotation is important for identifying, monitoring, and understanding sociological trends. Although expert annotators set the gold standard, they are expensive and inefficient. While state-of-the-art NLP models are an attractive alternative, they are often evaluated on standalone subtasks rather than entire workflows. Thus, we evaluate a holistic workflow that summarizes news with event coreference resolution and argument extraction in three modes: AI-only, AI assistance, and human only. Although AI{'}s recall is seven times higher than the tf-idf baseline at coreference resolution, it is far from replacing experts. However, experts adopt AI-extracted arguments 60{\%} of the time, reducing extraction time by 25{\%}. Our code and data are in https://github.com/Obertura777/gtd-data."
}