QuMAB: Query-based Multi-Annotator Behavior Modeling with Reliability under Sparse Labels

• URL: http://arxiv.org/abs/2507.17653v2
• Date: Thu, 07 Aug 2025 15:17:29 GMT
• Title: QuMAB: Query-based Multi-Annotator Behavior Modeling with Reliability under Sparse Labels
• Authors: Liyun Zhang, Zheng Lian, Hong Liu, Takanori Takebe, Yuta Nakashima,
• Abstract summary: Multi-annotator learning traditionally aggregates diverse annotations to approximate a single ground truth, treating disagreements as noise.<n>We introduce a paradigm shift from sample-wise aggregation to annotator-wise behavior modeling.<n>By treating annotator disagreements as valuable information rather than noise, modeling annotator-specific behavior patterns can reconstruct unlabeled data to reduce annotation cost, enhance aggregation reliability, and explain annotator decision behavior.
• Score: 23.555446749682467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-annotator learning traditionally aggregates diverse annotations to approximate a single ground truth, treating disagreements as noise. However, this paradigm faces fundamental challenges: subjective tasks often lack absolute ground truth, and sparse annotation coverage makes aggregation statistically unreliable. We introduce a paradigm shift from sample-wise aggregation to annotator-wise behavior modeling. By treating annotator disagreements as valuable information rather than noise, modeling annotator-specific behavior patterns can reconstruct unlabeled data to reduce annotation cost, enhance aggregation reliability, and explain annotator decision behavior. To this end, we propose QuMAB (Query-based Multi-Annotator Behavior Pattern Learning), which uses light-weight queries to model individual annotators while capturing inter-annotator correlations as implicit regularization, preventing overfitting to sparse individual data while maintaining individualization and improving generalization, with a visualization of annotator focus regions offering an explainable analysis of behavior understanding. We contribute two large-scale datasets with dense per-annotator labels: STREET (4,300 labels/annotator) and AMER (average 3,118 labels/annotator), the first multimodal multi-annotator dataset. Extensive experiments demonstrate the superiority of our QuMAB in modeling individual annotators' behavior patterns, their utility for consensus prediction, and applicability under sparse annotations.

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