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Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs
Speech-LLMs are expensive to run, making compression important for real-world deployment. However, compressed models are usually selected using aggregate word error rate (WER), which can hide how pruning affects different demographic groups. In this work, we systematically study the effect of audio
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