arXiv:2606.01034v1 Announce Type: new Abstract: We study when LLM judge panels should be calibrated with low-dimensional stackers versus joint output tables under finite human-label budgets. Low-dimensional stackers have small estimation cost but miss interactions, whereas joint-table calibrators can represent interactions but pay for cell counts and unseen patterns. We cast this tradeoff as a finite-calibration regime map and instantiate it as Finite-Calibration Panel Selection, a deployable validation selector over judge path, prefix size, and aggregator family with table and parametric esti
The proliferation of LLM judges in research and commercial applications necessitates robust validation and calibration methodologies.
Improving the reliability and cost-effectiveness of LLM judge panels directly impacts the development, evaluation, and safety of AI systems.
This research provides a framework for optimizing the calibration of LLM judge panels, potentially leading to more accurate and efficient AI evaluation processes.
- · AI developers
- · ML researchers
- · Companies using LLM-based evaluation
- · AI safety researchers
- · Inefficient AI evaluation methods
- · Developers unable to calibrate LLM judges effectively
More accurate and reliable LLM-based evaluation becomes standard practice in AI development.
Faster iteration cycles for AI models due to efficient, high-quality automated feedback loops, accelerating AI progress.
Enhanced trust and broader adoption of AI across critical sectors as evaluation biases and errors are systematically reduced.
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Read at arXiv cs.CL