arXiv:2606.15029v1 Announce Type: new Abstract: LLM judges are used to reduce the need for costly human labor in evaluating open-ended text generation. However, the reliability of these judges depends critically on their alignment with human raters -- a property that itself depends on costly human annotations. In this work, we develop a method (Metric Match) for estimating correlation-based reliability metrics of LLM judges from limited annotations. Metric Match selects a subset of samples for human annotation such that the subset matches the population reliability metric with respect to acqui
The proliferation of LLM judges necessitates more efficient and reliable evaluation methods to scale their deployment and integrate them into critical workflows.
Reliably evaluating LLM judges with limited human annotation reduces costs and accelerates the development and deployment of advanced AI systems, particularly autonomous agents.
The ability to accurately estimate LLM judge reliability with fewer human resources shifts the resource allocation for AI development and quality assurance.
- · AI developers
- · Companies using LLM judges
- · AI research institutions
- · Autonomous agent developers
- · Companies reliant on extensive human annotation services
- · LLM judges with poor intrinsic reliability
More widespread and cost-effective adoption of LLM judges for text generation evaluation.
Accelerated development cycles for AI models, especially those involving open-ended text generation and agentic systems.
Increased trust and reliance on AI-driven evaluation, potentially leading to fully autonomous AI quality control systems.
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Read at arXiv cs.AI