Revisiting Active Learning Under (Human) Label Variation
MCML Authors
Bernd Bischl
Prof. Dr.
Director
Göran Kauermann
Prof. Dr.
Principal Investigator
Barbara Plank
Prof. Dr.
Principal Investigator
Matthias Aßenmacher
Dr.
Abstract
Bernd Bischl
Prof. Dr.
Director
Göran Kauermann
Prof. Dr.
Principal Investigator
Barbara Plank
Prof. Dr.
Principal Investigator
Matthias Aßenmacher
Dr.
Abstract
Access to high-quality labeled data remains a limiting factor in applied supervised learning. Active learning (AL), a popular approach to optimizing the use of limited annotation budgets in training ML models, often relies on at least one of several simplifying assumptions, which rarely hold in practice when acknowledging human label variation (HLV). Label variation (LV), i.e., differing labels for the same instance, is common, especially in natural language processing. Yet annotation frameworks often still rest on the assumption of a single ground truth, overlooking HLV, i.e., the occurrence of plausible differences in annotations, as an informative signal. In this paper, we examine foundational assumptions about truth and label nature, highlighting the need to decompose observed LV into signal (e.g., HLV) and noise (e.g., annotation error). We survey how the AL and (H)LV communities have addressed—or neglected—these distinctions and propose a conceptual framework for incorporating HLV throughout the AL loop, including instance selection, annotator choice, and label representation. We further discuss the integration of large language models (LLM) as annotators. Our work aims to lay a conceptual foundation for (H)LV-aware active learning, better reflecting the complexities of real-world annotation.
inproceedings GAB+25
NLPerspectives @EMNLP 2025
4th Workshop on Perspectivist Approaches to NLP at the Conference on Empirical Methods in Natural Language Processing. Suzhou, China, Nov 04-09, 2025.Authors
C. Gruber • H. Alber • B. Bischl • G. Kauermann • B. Plank • M. AßenmacherLinks
DOIResearch Areas
BibTeXKey: GAB+25