LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation (2026.acl-long)
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| Challenge: | Existing methods for implementing large language models are limited by high computational and memory requirements. |
| Approach: | They propose a lightweight binarization framework that achieves effective W(1+1)A4 quantization through a novel three-stage quantization strategy. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on W2A4 quantization settings across languages. |
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| Approach: | They propose a post-training quantization framework with W(1+1)A(14) configuration . they propose utilizing Hessian-aware fine-grained grouping along with an EM-based quantization scheme . |
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| Challenge: | Large language models have driven major progress in NLP, but memory and compute requirements hinder practical deployment. |
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LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2024.findings-acl)
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Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Several post-training quantization methods have been shown to perform well down to 8-bits. |
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| Challenge: | Large language models (LLMs) are proficient in natural language processing tasks, but their deployment is limited by extensive parameter sizes and computational demands. |
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| Challenge: | Quantization studies have focused on instruction-tuned LLMs, leaving their performance on other benchmarks unclear. |
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LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices (2025.naacl-long)
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| Challenge: | Existing methods for quantizing weights and activations of large language models suffer from non-negligible accuracy drops, especially on massive multitask language understanding. |
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PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for sub 2-bit quantization introduce an extra 1-bit or more per weight. |
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| Challenge: | Quantization-aware Training (QAT) is a popular technique for reducing memory usage and improving computational efficiency in large language models. |
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PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models (2026.acl-srw)
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| Challenge: | Quantization Aware Training (QAT) is expensive to train and unscalable to large models. |
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DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)
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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models . |
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