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Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation (2024.findings-emnlp)

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Challenge: In subjective tasks, the inclusion of diverse annotators is crucial as their unique perspectives significantly influence the annotations.
Approach: They propose a framework that minimizes the annotation budget while maximizing the predictive performance for each annotator.
Outcome: The proposed framework surpasses the previous SOTA in capturing the annotators’ individual perspectives with as little as 25% of the original annotation budget on two datasets.

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