arXiv Machine Learning September 22, 2026 By Leonard Twagirayezu, Prasenjit Mitra

The Effect of Quantization on Clinical Benchmarks: Accuracy and Safety Across Model Families

Read the original on arXiv Machine Learning →

The study evaluates how quantization affects accuracy and safety of five 7‑8B language models on clinical benchmarks. INT8 GPTQ shows minimal degradation (≤1.9%) across tasks, while INT4 causes substantial, model‑dependent drops, especially in high‑risk scenarios and safety metrics. Recovery methods such as clinical calibration substitution and QLoRA fine‑tuning yield mixed results, underscoring the need for task‑specific validation.

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