Papers with Post-training quantization
Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling (2023.emnlp-main)
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| Challenge: | asymmetric outliers in transformer language models are a challenge for post-training quantization . we propose a framework for outlier suppression that can be seamlessly migrated into subsequent modules . |
| Approach: | They propose a framework for post-training quantization that includes the channel-wise shifting and scaling for concentration. |
| Outcome: | The proposed framework can be migrated into subsequent modules while maintaining equivalence. |
LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for post-training quantization (PTQ) are limited by the complexity of the quantization parameter and performance degradations when tested on unseen datasets. |
| Approach: | They propose a learnable smooth-based PTQ framework that allows for rapid adaptation during testing. |
| Outcome: | The proposed framework improves performance on unseen datasets and reduces memory constraints. |
Beyond Dynamic Quantization: An Efficient Static Hierarchical Mix-precision Framework for Near-Lossless LLM Compression (2025.emnlp-industry)
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| Challenge: | Existing methods for dynamic quantization are hardware-unfriendly and often lead to large quantization errors in static scenarios. |
| Approach: | They propose a Static Hierarchical Mix-precision Quantization method which quantifies both inter-layer and intra-layer sensitivity through unified derivations involving Hessian. |
| Outcome: | The proposed method achieves 75.58% on zero-shot reasoning tasks while yielding average speedup of 2.86. |
Theory-optimal Quantization Based on Flatness (2026.acl-long)
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| Challenge: | Recent approaches to quantization of Large Language Models (LLMs) have been widely adopted due to activation outliers, which degrade model performance especially at lower bit precision. |
| Approach: | They propose a new metric for quantization that strategically distributes outlier magnitudes across matrix dimensions via optimized diagonal operations. |
| Outcome: | The proposed framework achieves less than 1% accuracy drop in W4A4 quantization on the LLaMA-3-8B model and reduces the performance gap by 39.1% on the more challenging W2A4KV16 model. |
ACBQ: Adaptive Cross-Block Quantization of Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for post-training quantization struggle to support weight–activation joint quantization and extreme low-bit weight quantization. |
| Approach: | They propose a framework that addresses weight–activation joint quantization and extreme weight quantization. |
| Outcome: | The proposed framework achieves superior performance under both W4A4 and highly aggressive W2 settings while incurring negligible additional computational overhead. |
Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models (2026.acl-long)
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| Challenge: | Existing methods to quantize large language models suffer from significant accuracy loss at low bit-widths due to high-impact parameters. |
| Approach: | They propose a quadratic optimization framework that quantizes high-impact parameters to moderate bit-widths while quantizing low bit-wideths. |
| Outcome: | The proposed framework preserves high-impact parameters while preserving memory usage. |