CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion

• URL: http://arxiv.org/abs/2607.05046v1
• Date: Mon, 06 Jul 2026 13:22:04 GMT
• Title: CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion
• Abstract summary: Collaborative Evaluation (CollabEval) is a simple, effective, and principled method for exploiting dependencies between historical runs of different models on the same tasks to improve statistical efficiency.<n>We build a low-rank approximation of the score matrix, and use the reconstructed values to guarantee unbiased estimates of the true evaluation metric mean.<n>We find that CollabEval substantially reduces the mean confidence interval size, and the mean squared error of the point estimate, compared to baseline methods at the same annotation budget.
• Score: 65.53153291902454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating generative AI models is a routine, but resource-intensive, process that is conducted over and over again during the course of model development. In this work, we propose Collaborative Evaluation (CollabEval), a simple, effective, and principled method for exploiting dependencies between historical runs of different models on the same tasks to improve statistical efficiency. Specifically, our approach treats model evaluation as a matrix completion problem over an $M \times N$ matrix of evaluation scores, where $M$ is the total number of models and $N$ is the total number of evaluation prompts. We assume that a subset of these $M$ models are targeted for evaluation. For these target models only a small fraction, $p$, of prompts has been annotated with evaluation scores. Leveraging recent results in prediction-powered inference, we build a low-rank approximation of the score matrix, and use the reconstructed values as control variates in a manner that guarantees unbiased estimates of the true evaluation metric mean, in addition to statistically valid confidence intervals. Empirically, across a wide range of datasets, models, and sparsity levels $p$, we find that CollabEval substantially reduces the mean confidence interval size, and the mean squared error of the point estimate, compared to baseline methods at the same annotation budget.

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