English(EN) The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation

新研究批评数据标注中的“共识陷阱”和“真实依据”幻觉

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

一篇新论文批评了机器学习数据标注中“真实依据”的概念，认为人类的分歧常常被视为噪音而非有价值的信号。研究强调了位置可读性、对模型辅助标注的依赖以及地理霸权等因素如何导致“共识陷阱”。作者提出，应从寻求单一正确答案转变为描绘人类经验的多样性，以构建更具文化适应性的AI模型。 AI

影响挑战了AI训练数据中“真实依据”的观念，可能影响未来模型在文化适应性方面的评估和开发。

排序理由该集群包含一篇在arXiv上发表的学术论文。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CL TIER_1 English(EN) · Sheza Munir, Benjamin Mah, Krisha Kalsi, Shivani Kapania, Julian Posada, Edith Law, Ding Wang, Syed Ishtiaque Ahmed · 2026-04-28 04:00

共识陷阱：剖析数据标注中的主观性与“地面真实”的幻觉

arXiv:2602.11318v3 Announce Type: replace-cross Abstract: In machine learning, "ground truth" refers to the assumed correct labels used to train and evaluate models. However, the foundational "ground truth" paradigm rests on a positivistic fallacy that treats human disagreement a…