@inproceedings{moriyama-etal-2026-task,
    title = "Task Assignment meets Annotator Modeling: Human-{LLM} Collaborative Annotation with Constraints",
    author = "Moriyama, Kei  and
      Nakayama, Kouta  and
      Baba, Yukino",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-form-platform/2026.acl-srw.79/",
    pages = "888--902",
    ISBN = "979-8-89176-393-7",
    abstract = "Crowdsourced annotators and Large Language Models (LLMs) offer complementary, cost-effective ways to obtain labeled data, yet ensuring high label quality remains challenging.We observe that task features influence the accuracy of humans and LLMs, while real-world constraints, such as per-annotator assignment limits, further complicate allocation.Prior work typically addresses either task features or constraints, but not both.We present an integrated framework that (i) estimates per-task accuracy from task features using a \textit{learning from crowds} model and (ii) incorporates these estimations into a linear programming formulation that assigns tasks under practical constraints. Experimental results demonstrate that the proposed method achieves accuracy comparable to that of baseline methods while satisfying given constraints."
}