Language-Conditional Dequantization: Recovering What Quantization Steals from Non-English Languages

• URL: http://arxiv.org/abs/2608.11786v1
• Date: Wed, 12 Aug 2026 08:28:03 GMT
• Title: Language-Conditional Dequantization: Recovering What Quantization Steals from Non-English Languages
• Abstract summary: In the sub-4B INT3 GPTQ regime, we measure 2-4x larger perplexity degradation on non-English languages than on English.<n>We propose Language-Conditional Dequantization (LCD), a method that attaches per-language rank-2 LoRA corrections to the linear layers of an already-quantized model.<n>Across Qwen2.5-3B and Llama-3.2-3B, LCD recovers 70-83% of the perplexity gap for non-Latin script languages and 17-28% of the GlobalMMLU accuracy gap.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aggressive quantization disproportionately harms multilingual capability: in the sub-4B INT3 GPTQ regime, we measure 2-4x larger perplexity degradation on non-English languages than on English. We propose Language-Conditional Dequantization (LCD), a post-hoc method that attaches per-language rank-2 LoRA corrections to the linear layers of an already-quantized model, adding 0.12% parameters per language and training in under 20 minutes on a single GPU. Across Qwen2.5-3B and Llama-3.2-3B, LCD recovers 70-83% of the perplexity gap for non-Latin script languages and 17-28% of the GlobalMMLU accuracy gap, outperforming a language-agnostic correction of equal capacity by 3-9 points on typologically distant languages and a data-free low-rank baseline (LQER) by an order of magnitude. We further identify a perplexity-accuracy disconnect and trace it to where quantization concentrates damage: early-depth errors (Llama) propagate downstream and resist local correction, while late-depth errors (Qwen) do not. A layer-restricted variant of LCD validates this mechanism directly.

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