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        <title>Task Assignment meets Annotator Modeling: Human-LLM Collaborative Annotation with Constraints</title>
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    <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 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.</abstract>
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%0 Conference Proceedings
%T Task Assignment meets Annotator Modeling: Human-LLM Collaborative Annotation with Constraints
%A Moriyama, Kei
%A Nakayama, Kouta
%A Baba, Yukino
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F moriyama-etal-2026-task
%X 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 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.
%U https://aclanthology.org/2026.acl-srw.79/
%P 888-902

Markdown (Informal)

[Task Assignment meets Annotator Modeling: Human-LLM Collaborative Annotation with Constraints](https://aclanthology.org/2026.acl-srw.79/) (Moriyama et al., ACL 2026)

• Task Assignment meets Annotator Modeling: Human-LLM Collaborative Annotation with Constraints (Moriyama et al., ACL 2026)

ACL

• Kei Moriyama, Kouta Nakayama, and Yukino Baba. 2026. Task Assignment meets Annotator Modeling: Human-LLM Collaborative Annotation with Constraints. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 888–902, San Diego, California, United States. Association for Computational Linguistics.