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

arXiv: https://arxiv.org/abs/2510.12817 · HTML: https://mlsanity.com/paper/2510.12817/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 72 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 30 of 38)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging](https://mlsanity.com/paper/2310.11564/) (2023)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [HelpSteer2: Open-source dataset for training top-performing reward models](https://mlsanity.com/paper/2406.08673/) (2024)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://mlsanity.com/paper/2010.03532/) (2020)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [DICES Dataset: Diversity in Conversational AI Evaluation for Safety](https://mlsanity.com/paper/2306.11247/) (2023)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences](https://mlsanity.com/paper/2406.08469/) (2024)
- [Diverging Preferences: When do Annotators Disagree and do Models Know?](https://mlsanity.com/paper/2410.14632/) (2024)
- [VariErr NLI: Separating Annotation Error from Human Label Variation](https://mlsanity.com/paper/2403.01931/) (2024)
- [A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges](https://mlsanity.com/paper/2105.12949/) (2021)
- [Value Profiles for Encoding Human Variation](https://mlsanity.com/paper/2503.15484/) (2025)
- [Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback](https://mlsanity.com/paper/2410.19133/) (2024)
- [The Externalities of Exploration and How Data Diversity Helps Exploitation](https://mlsanity.com/paper/1806.00543/) (2018)
- [From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification](https://mlsanity.com/paper/2310.11878/) (2023)
- [Position: Insights from Survey Methodology can Improve Training Data](https://mlsanity.com/paper/2403.01208/) (2024)
- [STAR: SocioTechnical Approach to Red Teaming Language Models](https://mlsanity.com/paper/2406.11757/) (2024)
- [Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models](https://mlsanity.com/paper/2011.02998/) (2020)
- [Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF](https://mlsanity.com/paper/2502.15145/) (2025)
- [Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification](https://mlsanity.com/paper/2402.07214/) (2024)

## Built on by (most cited first, 3 of 3)

- [LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human-LLM Judgment Gaps](https://mlsanity.com/paper/2604.27345/) (2026)
- [Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEM](https://mlsanity.com/paper/2604.08425/) (2026)
- [LP-Eval: Rubric and Dataset for Measuring the Quality of Legal Proposition Generation](https://mlsanity.com/paper/2605.19815/) (2026)

## Abstract (arXiv)

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.