New estimator tackles LLM judge error correlation with contaminated anchors

A new paper introduces a closed-form estimator designed to address error correlation in LLM-judge panels when external reference sets (anchors) are used. The research focuses on scenarios where the anchor itself might be contaminated by the judges' shared error, a common assumption violation. The proposed method allows for the estimation of quality variance, common-mode variance, and anchor contamination correlation, even when the anchor is not perfectly clean. The estimator is accompanied by a diagnostic battery to assess model adequacy and identify potential biases. AI

IMPACT Provides a new statistical method for evaluating LLM outputs, potentially improving the reliability of benchmark results.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Veerendra Kumar Sunkavalli

• paper
• model release

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