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原文摘要
Large language models are increasingly used to classify text at scale. Because their outputs are stochastic, it is common to query a model several times per item and aggregate the results, and latent class models offer a principled way to convert such repeated ratings into estimates of outcome prevalence and of the model's error rates. These models assume that repeated ratings of the same item are conditionally independent given the true label. We examine this assumption using 52,500 judgments from seven open-weight models ranging from 1B to 32B parameters, collected under three prompt conditions with ten samples per item on a public benchmark with human labels. On the balanced split of the benchmark, intraclass correlations lie between 0.50 and 0.93, so ten ratings carry the information of between 1.07 and 1.81 independent ratings. Under this correlation, a latent class model with a binomial likelihood is both miscalibrated and biased. Its nominal 95% intervals for the false acceptance rate contained the value computed from human labels in none of 42 settings, and it underestimated the total error rate in all 42. We propose a beta-binomial latent class model with a single correlation parameter, establish its identifiability and that of extensions to covariates, treatment effects and partially labeled data, and show that on the balanced split it raises the coverage of nominal 95% intervals for the prevalence and the false acceptance rate to 0.90. Recovery of the latent state is governed by the gap between the model's acceptance rates on truly positive and truly negative items, which equals Youden's J and predicts recovery almost monotonically (Spearman correlation 0.96). When this gap is small, a labeled subset improves estimates of prevalence but not the classification of individual items. Simulation studies designed around the identification conditions support these findings.