新的QUORUM框架优化LLM和人类数据标注

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了QUORUM，一个旨在优化自然语言处理任务数据标注流程的新框架。QUORUM在指定预算内动态地将实例分配给人类标注员或大型语言模型（LLM）。它通过使用基于特征的信号来估计实例难度，而不是依赖模型置信度，从而实现差异化，并支持合并多个标注以提高可靠性。评估显示，与现有方法相比，QUORUM可将标注质量提高高达34.4%，同时降低成本8.8%。 AI

影响该框架可以显著提高LLM数据标注的效率和成本效益。

排序理由该集群包含一篇详细介绍数据标注新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍数据标注新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu, Amin Mantrach, Fabrizio Silvestri · 2026-08-31 04:00

QUORUM：使用多个标注器进行质量优化路由

arXiv:2608.27974v1 Announce Type: new Abstract: Data annotation remains a central bottleneck in natural language processing, requiring human effort to obtain high-quality labels at scale. While Large Language Models (LLMs) offer a fast and cost-effective alternative, their reliab…

本文如何被排名

报道来源 [1]

QUORUM：使用多个标注器进行质量优化路由

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