Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation
This work proposes a semantic-aware mixed-effects regression framework for fairness evaluation in LALMs that incorporates sentence-level semantic embeddings of reference text as covariates and models speaker identity as a random effect.