Human Label Variation (HLV) refers to legitimate disagreement in annotation that reflects the diversity of human perspectives rather than mere error. Long treated in NLP as noise to be eliminated, HLV has only recently been reframed as a signal for improving model robustness. With the rise of large language models (LLMs) and post-training methods such as human feedback-based alignment, the role of HLV has become increasingly consequential. Yet current preference-learning datasets routinely collapse multiple annotations into a single label, flattening diverse perspectives into artificial consensus. Preserving HLV is necessary not only for pluralistic alignment but also for sociotechnical safety evaluation, where model behavior must be assessed in relation to human interaction and societal context. This position paper argues that preserving HLV as an embodiment of human pluralism must be treated as a Selbstzweck, an intrinsic value in itself. We analyze the limitations of existing preference datasets and propose actionable strategies for incorporating HLV into dataset construction to better preserve pluralistic human values.

翻译：人类标签变异（HLV）指的是注释中正当的分歧，它反映了人类视角的多样性，而不仅仅是错误。长期以来，人类标签变异在自然语言处理中被视为需要消除的噪声，直到最近才被重新定位为提升模型鲁棒性的信号。随着大型语言模型和基于人类反馈对齐等后训练方法的兴起，人类标签变异的作用日益重要。然而，当前偏好学习数据集通常将多个标注压缩为单一标签，将多元视角扁平化为人为共识。保留人类标签变异不仅是实现多元对齐的必要条件，也是社会技术安全评估的需求——在此评估中，模型行为必须结合人类交互与社会背景进行研判。本立场论文主张，将人类标签变异作为人类多元性的具身表现，必须视为一种自在目的（Selbstzweck），即其本身具有内在价值。我们分析了现有偏好数据集的局限性，并提出了将人类标签变异纳入数据集构建的可操作策略，以更好地保留多元人类价值观。