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Evidence record 5084 · automatically gathered

Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels

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 compression on model quality remains poorly understood. Existing studies typically compare only two conditions (full-precision vs. a single quantized variant), rely on aggregate bias metrics, and evaluate a single model family, making it impossible to distinguish gradual degradation from threshold-dependent safety failu

Record details

Published: 2 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (2 May 2026), “Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels,” evidence record 5084, https://ethics.ai/record/5084 (originally published by arXiv).

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