标题： 从噪声到信号到目的本身：重新定义NLP后训练时代的人类标签变化 显示英文标题

标题： From Noise to Signal to Selbstzweck: Reframing Human Label Variation in the Era of Post-training in NLP

摘要： 人类标签差异（HLV）指的是在标注中合法的分歧，这种分歧反映了人类观点的真实多样性，而不是单纯的错误。 几十年来，自然语言处理（NLP）中的HLV被当作需要丢弃的噪声，直到最近十年才逐渐被重新视为提高模型鲁棒性的信号。 随着大型语言模型（LLMs）的兴起，对人类反馈进行训练已成为模型对齐的核心，HLV的作用变得越来越重要。 然而，当前的偏好学习数据集通常将多个标注合并为一个标签，从而将多样化的观点扁平化为虚假的普遍一致，抹去了对齐旨在保留的人类价值观的多元性。 在本文中，我们主张将HLV作为人类多元主义的体现，必须被视为一种目的本身——在设计人工智能系统时自身的目标。 我们呼吁主动将HLV纳入偏好数据集中，并提出了实现这一目标的可行步骤。 显示英文摘要

摘要： Human Label Variation (HLV) refers to legitimate disagreement in annotation that reflects the genuine diversity of human perspectives rather than mere error. For decades, HLV in NLP was dismissed as noise to be discarded, and only slowly over the last decade has it been reframed as a signal for improving model robustness. With the rise of large language models (LLMs), where post-training on human feedback has become central to model alignment, the role of HLV has become increasingly consequential. Yet current preference-learning datasets routinely aggregate multiple annotations into a single label, thereby flattening diverse perspectives into a false universal agreement and erasing precisely the pluralism of human values that alignment aims to preserve. In this position paper, we argue that preserving HLV as an embodiment of human pluralism must be treated as a Selbstzweck - a goal it self when designing AI systems. We call for proactively incorporating HLV into preference datasets and outline actionable steps towards it.