LLMs can be statistically superior to humans at estimating group-level judgments on subjective tasks because of their low variance and decoupled representation-processing biases.
Language Resources and Evaluation , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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Disagreement in health-literacy annotations is driven by conceptual task difficulty rather than annotator differences, with social effects varying or reversing by agreement level, making perspectivist modeling necessary.
citing papers explorer
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From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?
LLMs can be statistically superior to humans at estimating group-level judgments on subjective tasks because of their low variance and decoupled representation-processing biases.
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Structured Disagreement in Health-Literacy Annotation: Epistemic Stability, Conceptual Difficulty, and Agreement-Stratified Inference
Disagreement in health-literacy annotations is driven by conceptual task difficulty rather than annotator differences, with social effects varying or reversing by agreement level, making perspectivist modeling necessary.