arXiv:2511.21140v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as scalable evaluators of model responses in lieu of human annotators. However, imperfect sensitivity and specificity of the LLM judges induce bias in naive evaluation scores. We propose a simple plug-in framework that corrects this bias and enables statistically principled uncertainty quantification. Our framework constructs confidence intervals that account for uncertainty from both the test dataset and a human-labeled calibration dataset. Additionally, it uses an adaptive strategy to alloc
The proliferation of LLMs as evaluators necessitates robust methods for bias correction and uncertainty quantification to ensure reliability and trust in their assessments.
Accurate and unbiased evaluation of AI models is critical for their development, deployment, and adoption, especially as LLMs replace human annotators in complex tasks.
The proposed framework introduces a statistically principled method to correct biases in LLM-as-a-judge evaluations, moving from naive scoring to more reliable confidence intervals.
- · AI developers
- · ML research community
- · Companies using LLMs for evaluation
- · Platforms providing biased evaluation tools
- · Applications relying on uncorrected LLM evaluations
Improved reliability and fairness in the evaluation of large language models.
Faster and more dependable iteration cycles for new AI models due to better evaluation signals.
Increased public and institutional trust in AI systems due to more transparent and robust evaluation methodologies.
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Read at arXiv cs.CL