HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
arXiv:2606.29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity. Organizations thus often turn to collecting vast but noisy "silver" labels from crowdsourced workers or vendor annotators as proxies for gold labels. Because gold remains the evaluation target, naively aggregating noisy silver labels may introduce bias, and estimators built on sparsely observed gold labels may have high variance to resolve the model performance gaps that
The proliferation of generative AI models necessitates robust and reliable evaluation methods, and current methods are often bottlenecked by expensive 'gold' standard human annotations.
Improving generative AI evaluation reliability and efficiency is critical for accelerating AI development, trust, and wider adoption across industries.
This research proposes a method to leverage abundant 'silver' (noisy) labels alongside scarce 'gold' labels, potentially making generative AI evaluation more scalable and less costly.
- · AI model developers
- · Organizations deploying generative AI
- · AI evaluation platforms
- · Companies reliant on expensive, purely gold-standard human annotation
Generative AI models can be evaluated more frequently and cost-effectively, leading to faster iteration cycles.
Higher quality and more reliable generative AI applications emerge due to improved evaluation, increasing market trust and adoption.
The reduced cost of evaluation could democratize access to advanced AI development, broadening the competitive landscape beyond well-funded entities.
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Read at arXiv cs.AI