Kuniko Paxton, Ifeoluwa Wuraola, Dhavalkumar Thakker · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29657 · open-access PDF
The deployment of small and quantized language models in resource-constrained settings raises an important fairness question: when a compressed model produces fewer biased responses, does this reflect fairer behavior or greater abstention? We study this problem in Question Answering (QA) using the BBQ benchmark across multiple model sizes and quantization settings from Qwen3, Gemma-3, and SmolLM2. We also examine whether the observed trends remain stable across alternative prompt formulations and when the ``Unknown'' option is removed in a forced-choice setting. Our results show that larger mo
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