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. |
Similar Papers
Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory. |
| Approach: | They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules. |
| Outcome: | The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin. |
pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for building efficient large language models with sub 2-bit weights are lacking in accuracy and scalability. |
| Approach: | They propose a method that decouples parameters by splitting linear layers into two specialized branches. |
| Outcome: | The proposed method achieves state-of-the-art performance in extremely low-bit quantization. |
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models (2022.findings-emnlp)
Copied to clipboard
Se Jung Kwon, Jeonghoon Kim, Jeongin Bae, Kang Min Yoo, Jin-Hwa Kim, Baeseong Park, Byeongwook Kim, Jung-Woo Ha, Nako Sung, Dongsoo Lee
| Challenge: | Existing approaches to improve inference efficiency by accelerating model fine-tuning have not been thoroughly explored. |
| Approach: | They propose to combine parameter-efficient adaptation and model compression to accelerate model . they propose to freeze binary parameters and scale scaling factors for target tasks . |
| Outcome: | The proposed algorithm achieves >10x compression ratio under 4-bit quantization and >1,000x reduction in trainable parameters. |
Exploring Quantization for Efficient Pre-Training of Transformer Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Quantization has proven to be effective after pre-training and during fine-tuning, but its effects on pre-trainer performance have remained unexplored. |
| Approach: | They propose a linear quantization strategy to be applied during the pre-training of Transformers to improve model efficiency and stability. |
| Outcome: | The proposed method improves model efficiency, stability, and performance while maintaining language modeling ability. |
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis (2022.findings-emnlp)
Copied to clipboard
Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Pre-trained language models (PLMs) have gained increasing popularity due to compelling prediction performance in diverse natural language processing tasks. |
| Approach: | They compare three popular options for encoding and Temp Scaling for PLMs . they recommend using Temp Loss as uncertainty quantifier and Focal Loss for fine-tuning . |
| Outcome: | Using pre-trained language models, we compare three options on NLP classification tasks and domain shift. |
LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models (2024.acl-long)
Copied to clipboard
| 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. |
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. |
On the Way to Lossless Compression of Language Transformers: Exploring Cross-Domain Properties of Quantization (2024.lrec-main)
Copied to clipboard
Nikita Martynov, Aleksei Goncharov, Gleb Kumichev, Evgeniy Egorov, Stanislav Vladimirovich Pavlov, Mikhail Sergeevich Durinov, Aleksandr Sergeevich Zuev, Egor Anatolievich Filimonov
| Challenge: | Modern Natural Language Processing models have a huge capacity, but this makes it difficult to employ. |
| Approach: | They propose a method to quantize at least 95% of Transformer weights without access to task-specific data so the drop in performance does not exceed 0.02%. |
| Outcome: | The proposed method quantizes 95% of Transformer weights and corresponding activations to INT8 without access to task-specific data so the drop in performance does not exceed 0.02%. |
A Comprehensive Evaluation of Quantization Strategies for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Quantization studies have focused on instruction-tuned LLMs, leaving their performance on other benchmarks unclear. |
| Approach: | They propose a framework to evaluate quantized large language models using four dimensions . they propose to reduce the bits needed for model weights or activations with minimal performance loss . |
| Outcome: | The proposed framework can retain comparable performance to non-quantized LLMs on most benchmarks. |
Revisiting Pruning vs Quantization for Small Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Compressing Small Language Models (SLMs) is particularly suited for resource-constrained devices, but their compression dynamics remain underexplored compared to Large Language Model (LLMs). |
| Approach: | They evaluated post-training pruning and quantization methods across six SLMs from 0.5 to 3.8B, seven languages, and seven downstream tasks. |
| Outcome: | The proposed methods outperform pruning and quantization on six SLMs from 0.5 to 3.8B, seven languages, and seven downstream tasks. |