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Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research shows pruning speech LLMs introduces demographic performance disparities hidden by aggregate word error rates.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 30 September 2026
- Collected by OneBench
- 1 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2609.38106v1 Announce Type: cross Abstract: Speech-LLMs are expensive to run, making compression important for real-world deployment. However, compressed models are usually selected using aggregate word…
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