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cs.LG2026
Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels
Plawan Kumar Rath, Rahul Maliakkal
Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deployment, yet the impact of this c…
cs.LG2026
Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI
Plawan Kumar Rath, Rahul Maliakkal
Weight pruning is widely advocated for deploying Large Language Models on resource-constrained IoT and edge devices, yet its impact on model fairness remains poorly understood. We…