Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, yet their significant computational and memory requirements present major challenges for deployment. A common approach uses Taylor expansion on the loss function to estimate neuron importance. However, its reliance on one-hot cross entropy loss, a key limitation is that it narrowly assesses importance based only on the probability assigned to the single predicted next token, thereby ignoring the other potential predictions of the original model. An intuitive solution to address this is to employ self distillation criterion for importance evaluation. However, this approach introduces significant computational overhead by requiring a separate teacher model for supervision. To this end, we propose a simple but effective criterion, information entropy of the model's output distribution, to efficiently evaluate importance scores of neurons with Taylor pruning without requirement of additional teacher. Compared to plain cross entropy criterion, it provides a more holistic criterion for Taylor pruning to prune neurons with the least impact on the prediction of model in a global manner, thereby preserving the fidelity of the model's predictive capabilities. Experimental results on extensive zero-shot benchmarks demonstrate that our method consistently outperforms existing pruning methods across the LLaMA and Qwen series models. The source code and trained weights are availabel at https://github.com/visresearch/HFPrune.
iFLYTEK Research（科大讯飞研究院）；Harbin Institute of Technology（哈尔滨工业大学）
Pre-trained language models have been prevailed in natural language processing and become the backbones of many NLP tasks, but the demands for computational resources have limited their applications. In this paper, we introduce TextPruner, an open-source model pruning toolkit designed for pre-trained language models, targeting fast and easy model compression. TextPruner offers structured post-training pruning methods, including vocabulary pruning and transformer pruning, and can be applied to various models and tasks. We also propose a self-supervised pruning method that can be applied without…
IG-Pruning: Input-Guided Block Pruning for Large Language Models
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)（智能信息处理重点实验室，计算技术研究所，中国科学院）；Key Laboratory of AI Safety, Chinese Academy of Sciences（人工智能安全重点实验室，中国科学院）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）
Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）
MI-PRUN: Optimize Large Language Model Pruning via Mutual Information
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）
The Chinese University of Hong Kong（香港中文大学）；Alibaba Group（阿里巴巴集团）；Peking University（北京大学）
Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts. This work investigates three aspects of structured pruning on multilingual pre-trained language models: settings, algorithms, and efficiency. Experiments on nine downstream tasks show several counter-intuitive phenomena: for settings, individually pruning for each language does not induce a better result; for algorithms, the simplest method performs the best; for efficiency, a fast model does not imply that it is also small. To facilita…
On the Limitations of Language Targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning
Department of Computer Science, TU Dortmund University（图恩-多特蒙德大学计算机科学系）；Bonn-Aachen International Center for Information Technology, University of Bonn（波恩-亚琛国际信息科技中心，波恩大学）；Computer Science School, University of Sheffield（谢菲尔德大学计算机科学学院）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）
Auto-regressive large language models such as GPT-3 require enormous computational resources to use. Traditionally, structured pruning methods are employed to reduce resource usage. However, their application to and efficacy for generative language models is heavily under-explored. In this paper we conduct an comprehensive evaluation of common structured pruning methods, including magnitude, random, and movement pruning on the feed-forward layers in GPT-type models. Unexpectedly, random pruning results in performance that is comparable to the best established methods, across multiple natural l…
Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency have significantly increased, which makes their usage costly, and raises an interesting question: do language models need to be large? We study this question through the lens of model compression. We present a generic, structured pruning approach by parameterizing each weight matrix using its low-rank factorization, and adaptively removing rank-1 components during training. On language modeling tasks, our structured app…
Efficient Post-Training Pruning of Large Language Models with Statistical Correction
Department of Computer Science and Engineering, Santa Clara University, Santa Clara, CA, USA（计算机科学与工程系，圣克拉拉大学）；Futurewei Technologies, Inc., USA（未来韦艾技术公司）
Systematic Weight Evaluation for Pruning Large Language Models: Enhancing Performance and Sustainability
organization= College Of Science \& Engineering, Hamad Bin Khalifa University , addressline= Education City , city= Doha , postcode= 34110 , country= Qatar
Fluctuation-based Adaptive Structured Pruning for Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Wuhan AI Research（武汉人工智能研究院）；Objecteye Inc.（Objecteye公司）
Pre-trained Language Models (PLMs) have achieved great success in various Natural Language Processing (NLP) tasks under the pre-training and fine-tuning paradigm. With large quantities of parameters, PLMs are computation-intensive and resource-hungry. Hence, model pruning has been introduced to compress large-scale PLMs. However, most prior approaches only consider task-specific knowledge towards downstream tasks, but ignore the essential task-agnostic knowledge during pruning, which may cause catastrophic forgetting problem and lead to poor generalization ability. To maintain both task-agnost…
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Baptist Univeristy（香港浸会大学）；Harbin Institude of Technology, Shenzhen（哈尔滨工业大学（深圳））
Frustratingly Easy Task-aware Pruning for Large Language Models
Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；University of Science and Technology of China（中国科学技术大学）；Xi’an Jiaotong University（西安交通大学）
Institute of Science and Technology Austria（奥地利科学技术研究所）；Neural Magic Inc.（Neural Magic公司）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Transformer-based language models have become a key building block for natural language processing. While these models are extremely accurate, they can be too large and computationally intensive to run on standard deployments. A variety of compression methods, including distillation, quantization, structured and unstructured pruning are known to decrease model size and increase inference speed, with low accuracy loss. In this context, this paper's contributions are two-fold. We perform an in-depth study of the accuracy-compression trade-off for unstructured weight pruning of BERT models. We in…
Pruning General Large Language Models into Customized Expert Models
National University of Singapore（国立新加坡大学）；Nanyang Technological University（南洋理工大学）；DAMO Academy, Alibaba Group（阿里达摩院）；MiroMind；Singapore University of Technology and Design（新加坡科技设计大学）
Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing applications. We propose the use of movement pruning, a simple, deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning. We give mathematical foundations to the method and compare it to existing zeroth- and first-order pruning methods. Experiments show that when pruning large pretrained language models, movement pruning…
Pruning as a Cooperative Game: Surrogate-Assisted Layer Contribution Estimation for Large Language Models
Shenzhen Future Network of Intelligence Institute（深圳未来网络智能研究院）；Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来网络智能重点实验室）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Beijing Normal University（北京师范大学）；Alibaba Group（阿里巴巴集团）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang University（浙江大学）
