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#artificial intelligence Preprint Oct 2026

A CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge

Large language models (LLMs) are increasingly used as judges to predict which of two candidate answers a human evaluator would prefer, providing a scalable alternative when human evaluation is too costly. Unfortunately, LLM judgments can exhibit systematic biases. Prior work has documented position, verbosity or model...

Cesare Barbera, Luca Stradiotti, Lorenzo Cascioli et al. · 0 citations
#artificial intelligence Review Apr 2026

Multicalibration for Unbiased Model-Based Prevalence Estimation

Two empirical applications -- estimating employment prevalence across U.S. states using the American Community Survey and classifying political texts across four countries using an LLM -- demonstrate that multicalibration substantially reduces bias in practice, while highlighting that calibration data should cover the...

Fridolin Linder, Thomas J. Leeper, Daniel Haimovich et al. · 1 citation

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