arXiv:2606.15610v1 Announce Type: new Abstract: LLM-as-a-judge systems are now routinely used for open-ended model evaluation, where human preference annotation is costly, slow, and difficult to reproduce. Yet these judges are often reported as scalar accuracy, win-rate, or agreement devices. We argue that a judge should instead be reported as a measurement instrument. We introduce a Judge Datasheet protocol that measures dark current under true-vacuum inputs, stable cross-sensitivity to same-quality surface variation, positional false preference, target sensitivity on a controlled quality lad
The proliferation of LLM-as-a-judge systems for evaluating open-ended model performance necessitates more rigorous, psychometric-driven evaluation protocols to address existing limitations and biases.
A robust, standardized methodology for LLM evaluation through a 'judge datasheet' can significantly improve the development and deployment of reliable AI systems. This introduces transparency and accountability for AI developers and users.
The shift from scalar metrics to a psychometric datasheet approach will standardize how LLM judges are assessed, leading to more trustworthy and reproducible evaluations of foundational models. This could refine benchmarks and development practices.
- · AI developers focused on quality assurance
- · AI ethicists and regulatory bodies
- · Organizations deploying AI in critical applications
- · AI developers relying on superficial evaluation metrics
- · Black-box LLM-as-a-judge systems without proper validation
Improved reliability and safety of AI models due to better evaluation methods.
Increased trust in AI systems could accelerate adoption in sensitive sectors, leading to new market opportunities.
The development of 'AI auditing' as a new professional field, focused on validating and certifying LLM judge performance.
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