Papers with Quantization
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit (2024.emnlp-industry)
Copied to clipboard
Ruihao Gong, Yang Yong, Shiqiao Gu, Yushi Huang, Chengtao Lv, Yunchen Zhang, Dacheng Tao, Xianglong Liu
| Challenge: | Existing quantization techniques have been categorized as 'simple' and 'highly efficient' however, their configurations vary from each other and cannot be fairly compared . |
| Approach: | They propose a plug-and-play compression toolkit to explore the impact of quantization. |
| Outcome: | The proposed toolkit explores the impact of quantization on large language models. |
PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models (2026.acl-srw)
Copied to clipboard
| Challenge: | Quantization Aware Training (QAT) is expensive to train and unscalable to large models. |
| Approach: | They propose a parameter-efficient framework targeting per-channel 4-bit weight-activation quantization of large language models. |
| Outcome: | The proposed framework preserves accuracy within 0.11 percentage points of the full-precision baseline on Llama-2-7B zero-shot tasks while training only 1.26% of total parameters. |
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations (2025.coling-main)
Copied to clipboard
| Challenge: | Large language models require substantial storage space to perform tasks such as text generation and video generation. |
| Approach: | They propose to compress large language models using integer replacements for floating-point numbers, in a process known as Quantization. |
| Outcome: | The proposed model allows for quantization of up to 75% decoder layers with 1 bit while maintaining performance levels comparable to those of the models with floating parameters. |
Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLMs (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing quantization methods typically use small, English-only calibration sets . however, their impact on multilingual models remains underexplored . |
| Approach: | They evaluate eight calibration settings across two quantizers on data from 10 different languages. |
| Outcome: | The results show that tailoring calibration sets to the evaluation language yields the largest improvements for individual languages, underscoring the importance of linguistic alignment. |
QSpec: Speculative Decoding with Complementary Quantization Schemes (2025.emnlp-main)
Copied to clipboard
| Challenge: | Quantization is widely adopted to accelerate inference and reduce memory consumption in large language models. |
| Approach: | They propose a quantization paradigm that decouples efficiency from quality by integrating two complementary schemes via speculative decoding. |
| Outcome: | The proposed approach achieves 1.64x speedup without quality degradation and outperforms state-of-the-art speculative decoding methods by 1.55x in batched settings. |
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks (2022.coling-1)
Copied to clipboard
| Challenge: | Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. |
| Approach: | They compare accuracy vs. model size tradeoffs using quantization and distillation methods . they find that pruning provides greater benefit than quantization . |
| Outcome: | The proposed methods reduce model size and can accelerate inference, but their relative benefit and combinatorial interactions have not been rigorously studied. |
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation (2020.findings-emnlp)
Copied to clipboard
Insoo Chung, Byeongwook Kim, Yoonjung Choi, Se Jung Kwon, Yongkweon Jeon, Baeseong Park, Sangha Kim, Dongsoo Lee
| Challenge: | Quantization is an effective technique to address heavy computation load and memory overhead during inference. |
| Approach: | They propose a low-bit quantization strategy to represent Transformer weights by an extremely low number of bits. |
| Outcome: | The proposed model achieves 11.8 smaller model size than baseline model, with less than -0.5 BLEU. |
Does quantization affect models’ performance on long-context tasks? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models support context windows exceeding 128K tokens, but this comes with significant memory requirements and high inference latency. |
| Approach: | They present the first systematic evaluation of quantized LLMs on tasks with long inputs and long-form outputs. |
| Outcome: | The proposed method preserves accuracy, while 4-bit methods lead to substantial losses . the results highlight the importance of a careful evaluation before deploying quantized LLMs . |
XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. however, their extensive memory requirements present significant challenges for deployment in resource-constrained environments. |
| Approach: | They propose a training-free framework that achieves ultra-low equivalent bit-width KV cache quantization. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on TruthfulQA and LongBench. |
Self-calibration for Language Model Quantization and Pruning (2025.naacl-long)
Copied to clipboard
| Challenge: | Quantization and pruning are fundamental approaches for model compression, but they require large computational resources. |
| Approach: | They propose to use model calibration data to generate synthetic calibrations to improve model performance. |
| Outcome: | The proposed method outperforms other methods using real data in a post-training setting. |
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models (2023.findings-acl)
Copied to clipboard
Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Rui Yan
| Challenge: | Quantization is a viable solution for pre-trained language models, but most existing methods are task-specific and require customized training and quantization with a large number of trainable parameters. |
| Approach: | They propose a "quantize before fine-tuning" framework that allows for quantization with a large number of trainable parameters on each individual task. |
| Outcome: | The proposed framework is compatible with quantization-aware training and post-training quantization and corrects quantization errors. |
On the Impact of Calibration Data in Post-training Quantization and Pruning (2024.acl-long)
Copied to clipboard
| Challenge: | Quantization and pruning are the foundations of compression for large language models . however, no prior work has investigated how calibration data impacts performance of compression methods. |
| Approach: | They propose an empirical study on the effect of calibration data on LLM performance. |
| Outcome: | The proposed methods improve performance in a post-training setting. |
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs . however, it is hindered by two critical challenges: substantial GPU memory consumption and low activated parameters. |
| Approach: | They propose an Expert-Selection Aware Compressor for Mixture-of-Experts (MoE) that aligns with the characteristics of MoE from the perspectives of quantization and pruning. |
| Outcome: | The proposed approach significantly reduces memory usage and improves inference speed with minimal performance degradation. |
Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models? (2025.findings-acl)
Copied to clipboard
| Challenge: | Quantization-aware training of large language models reduces the precision of model parameters and reduces memory usage and energy consumption at inference time. |
| Approach: | They propose a method where models are first trained with 16-bit precision and then transition to 1.58-bit quantization-aware training. |
| Outcome: | The proposed training strategy reduces memory and energy consumption while maintaining model accuracy while reducing memory and inference time. |
Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance. |
| Approach: | They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem. |
| Outcome: | The proposed framework can achieve significant performance gains on discriminative and generative PLMs. |
QEFT: Quantization for Efficient Fine-Tuning of LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to optimize inference and fine-tuning for large language models have failed to improve all aspects of the process. |
| Approach: | They propose a new technique that accelerates both inference and fine-tuning while using fewer resources. |
| Outcome: | The proposed technique accelerates both inference and fine-tuning while using fewer resources. |
Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing quantization methods focus on general metrics like perplexity or accuracy on standard benchmarks. |
| Approach: | They propose a method that preserves fairness- and safety-critical weights during quantization. |
| Outcome: | The proposed method reduces bias and safety degradation without costly retraining or alignment while maintaining trustworthiness while retaining efficiency. |
D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation (2026.findings-acl)
Copied to clipboard
Junlin Li, Shuangyong Song, Guodong DU, Ngai Wong, Xuebo Liu, Yongxiang Li, Min Zhang, Jing Li, Xuelong Li
| Challenge: | Existing methods for fine-tuned large language models fail on fine-scale datasets . large data scale amplifies delta parameter magnitude, singular values, and entropy, causing compression errors. |
| Approach: | They propose a training- and data-free delta compression method that captures dominant delta structure and compensates residual low-rank approximation to recover fine-grained details from smaller residual error. |
| Outcome: | The proposed method outperforms existing methods on large-scale datasets on dense and MoE architectures. |
Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models (2025.acl-long)
Copied to clipboard
Seungcheol Park, Jeongin Bae, Beomseok Kwon, Minjun Kim, Byeongwook Kim, Se Jung Kwon, U Kang, Dongsoo Lee
| Challenge: | Quantization is essential for deploying large language models (LLMs) efficiently since they require expensive computational and memory costs. |
| Approach: | They propose a quantization method that unifies flexible mapping techniques to optimize parameters precisely. |
| Outcome: | The proposed method outperforms existing methods and achieves higher accuracy on GSM8K benchmark. |
Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Quantization enables efficient deployment of large language models in resource-constrained environments . but impact on truthfulness remains largely unexplored . |
| Approach: | They propose a framework to assess the truthfulness of quantized large language models . they find quantized models retain internally truthful representations but produce false outputs . |
| Outcome: | The framework assesses the truthfulness of quantized models across three dimensions . it finds that quantized model models retain internally truthful representations but are more susceptible to false outputs . |
Fast and Accurate Fisher-Guided Quantization via Efficient Kronecker Factorization (2026.acl-long)
Copied to clipboard
| Challenge: | Quantization has shown strong results in preserving model quality under compression, but under aggressive bit-width reductions, even quantization may require additional information to prevent performance degradation. |
| Approach: | They propose a Kronecker-factored approximation that captures second-order curvature information, captured by the Hessian, to achieve a 10 speedup over prior approaches. |
| Outcome: | The proposed method significantly accelerates the most expensive component in second-order quantization – Hessian parameterization . it achieves up to a 10 speedup over prior approaches. |