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Annotation needs for AI models vary by evaluation metric, study finds

By PulseAugur Editorial · [2 sources] ·

A new research paper explores how the number of annotators needed to effectively train AI models depends on the specific evaluation metric used. The study, focusing on Natural Language Inference (NLI) models, found that metrics like entropy correlation require a larger annotator pool (20-50 individuals) to stabilize, while distributional match metrics like KL divergence converge with as few as 10 annotators. This suggests that annotation budgets should be tailored to the intended evaluation metric rather than using a uniform approach. AI

IMPACT Suggests optimizing annotation budgets based on evaluation metrics for more efficient AI model training.

RANK_REASON The cluster contains a research paper detailing new findings on AI model training methodologies.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Annotation needs for AI models vary by evaluation metric, study finds

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Guneet Kohli ·

    Metric-Dependent Annotation Saturation for Learning from Label Distributions

    arXiv:2605.29797v1 Announce Type: new Abstract: When annotators disagree on a label, the disagreement itself carries signal -- and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distributions subsampled from ChaosN…

  2. arXiv cs.CL TIER_1 English(EN) · Guneet Kohli ·

    Metric-Dependent Annotation Saturation for Learning from Label Distributions

    When annotators disagree on a label, the disagreement itself carries signal -- and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distributions subsampled from ChaosNLI, a dataset providing 100 independent annotato…

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