Validity of the conditional-independence assumption in real-world LLM annotation applications

Determine whether the conditional independence assumption required by debiased inference with multiple imperfect measurements (DMM)—that multiple LLM-generated proxy labels are independent conditional on the latent true label and observed annotation or downstream covariates—holds in the real-world empirical application involving annotations of prefecture-level wrongdoing in Chinese citizen complaints.

Background

The DMM framework identifies and corrects measurement error without gold-standard labels by assuming that multiple imperfect measurements are conditionally independent given the latent true label and observed features such as downstream covariates or annotation-task characteristics. In the empirical validation using three LLM annotations of prefecture-level wrongdoing, the authors compare DMM with naive estimators, a gold-standard-based design-based supervised-learning estimator, and an oracle estimator based on expert labels.

Because the empirical data are observational and the conditional independence assumption is not directly established by the application, the authors explicitly state that they do not know whether the assumption holds in this real-world setting. This unresolved validity question matters because DMM’s identification and inferential guarantees depend on the assumption, whereas the validation study only assesses how closely DMM performs relative to the expert-label benchmark in the available data.