@inproceedings{qin-etal-2025-crowdagent,
title = "{C}rowd{A}gent: Multi-Agent Managed Multi-Source Annotation System",
author = "Qin, Maosheng and
Zhu, Renyu and
Xia, Mingxuan and
Chenchenkai and
Zhu, Zhen and
Lin, Minmin and
Zhao, Junbo and
Xu, Lu and
Fan, Changjie and
Wu, Runze and
Wang, Haobo",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.72/",
pages = "925--942",
ISBN = "979-8-89176-334-0",
abstract = "High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources{---}including Large Language Models (LLMs), Small Language Models (SLMs), and human experts{---}they often focus narrowly on the labeling step itself. A critical gap remains in the holistic process control required to manage these sources dynamically, addressing complex scheduling and quality-cost trade-offs in a unified manner. Inspired by real-world crowdsourcing companies, we introduce CrowdAgent, a multi-agent system that provides end-to-end process control by integrating task assignment, data annotation, and quality/cost management. It implements a novel methodology that rationally assigns tasks, enabling LLMs, SLMs, and human experts to advance synergistically in a collaborative annotation workflow. We demonstrate the effectiveness of CrowdAgent through extensive experiments on six diverse multimodal classification tasks. The source code and video demo are available at https://github.com/QMMMS/CrowdAgent."
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%0 Conference Proceedings
%T CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
%A Qin, Maosheng
%A Zhu, Renyu
%A Xia, Mingxuan
%A Zhu, Zhen
%A Lin, Minmin
%A Zhao, Junbo
%A Xu, Lu
%A Fan, Changjie
%A Wu, Runze
%A Wang, Haobo
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%A Chenchenkai
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F qin-etal-2025-crowdagent
%X High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources—including Large Language Models (LLMs), Small Language Models (SLMs), and human experts—they often focus narrowly on the labeling step itself. A critical gap remains in the holistic process control required to manage these sources dynamically, addressing complex scheduling and quality-cost trade-offs in a unified manner. Inspired by real-world crowdsourcing companies, we introduce CrowdAgent, a multi-agent system that provides end-to-end process control by integrating task assignment, data annotation, and quality/cost management. It implements a novel methodology that rationally assigns tasks, enabling LLMs, SLMs, and human experts to advance synergistically in a collaborative annotation workflow. We demonstrate the effectiveness of CrowdAgent through extensive experiments on six diverse multimodal classification tasks. The source code and video demo are available at https://github.com/QMMMS/CrowdAgent.
%U https://aclanthology.org/2025.emnlp-demos.72/
%P 925-942
Markdown (Informal)
[CrowdAgent: Multi-Agent Managed Multi-Source Annotation System](https://aclanthology.org/2025.emnlp-demos.72/) (Qin et al., EMNLP 2025)
ACL
- Maosheng Qin, Renyu Zhu, Mingxuan Xia, Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, and Haobo Wang. 2025. CrowdAgent: Multi-Agent Managed Multi-Source Annotation System. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 925–942, Suzhou, China. Association for Computational Linguistics.