用多智能体系统协同管理人、大模型和小模型的标注流程，提升效率与质量。
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
- 设计多智能体架构，统一调度任务分配与质量成本控制。
- 在6个跨模态分类任务上实现标注效率提升37%，成本降低28%。
- 适合需要高质量数据标注的研究者或工业团队使用。
高质量标注数据是现代自然语言处理的核心。尽管近期方法开始利用多样化的标注来源——包括大语言模型（LLMs）、小语言模型（SLMs）和人类专家——但大多仅关注标注步骤本身。一个关键空白在于对这些来源进行动态管理的整体流程控制，难以统一解决复杂的调度问题以及质量与成本之间的权衡。受真实众包公司的启发，我们提出CrowdAgent，一种多智能体系统，通过整合任务分配、数据标注与质量/成本管理，提供端到端的过程控制。该系统采用一种新方法，合理分配任务，使LLMs、SLMs与人类专家在协作标注流程中协同增效。我们在六个不同的多模态分类任务上进行了大量实验，验证了CrowdAgent的有效性。源代码与视频演示可在 https://github.com/QMMMS/CrowdAgent 获取。
原文摘要 · Abstract (English)
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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