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：人类标注变异（HLV）指标注过程中存在的合理分歧，其反映的是人类视角的多样性而非单纯错误。长期以来，HLV在自然语言处理领域被视为需要消除的噪声，直至最近才被重新定义为提升模型鲁棒性的信号。随着大语言模型（LLMs）及基于人类反馈的对齐等后训练方法的兴起，HLV的作用日益凸显。然而当前偏好学习数据集通常将多重标注压缩为单一标签，使多元视角扁平化为人工共识。保留HLV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关键——在此类评估中，模型行为必须置于人机交互与社会语境中加以考量。本立场论文主张，将HLV作为人类多元性的具象予以保留，应被视为一种“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，并提出在数据集构建中纳入HLV的可操作策略，以更好地保存多元化的人类价值观。