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Related papers: Fairness-Aware Structured Pruning in Transformers

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Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks. However, these models contain enormous amounts of parameters, which restrict their deployment to real-world…

Machine Learning · Computer Science 2022-06-28 Qingru Zhang , Simiao Zuo , Chen Liang , Alexander Bukharin , Pengcheng He , Weizhu Chen , Tuo Zhao

Large Language Models (LLMs) have emerged as promising solutions for a variety of medical and clinical decision support applications. However, LLMs are often subject to different types of biases, which can lead to unfair treatment of…

Computation and Language · Computer Science 2024-08-23 Raphael Poulain , Hamed Fayyaz , Rahmatollah Beheshti

Large Language Models (LLMs) demonstrate exceptional reasoning abilities, enabling strong generalization across diverse tasks such as commonsense reasoning and instruction following. However, as LLMs scale, inference costs become…

Computation and Language · Computer Science 2025-02-06 Rhea Sanjay Sukthanker , Benedikt Staffler , Frank Hutter , Aaron Klein

Large language models (LLMs) have rapidly advanced in recent years, achieving remarkable performance across a wide range of natural language processing tasks. However, this progress has come at the cost of increasingly large model sizes,…

Due to its significant capability of modeling long-range dependencies, vision transformer (ViT) has achieved promising success in both holistic and occluded person re-identification (Re-ID) tasks. However, the inherent problems of…

Computer Vision and Pattern Recognition · Computer Science 2023-04-05 Junzhu Mao , Yazhou Yao , Zeren Sun , Xingguo Huang , Fumin Shen , Heng-Tao Shen

Large language models (LLMs) have proven to be highly effective across various natural language processing tasks. However, their large number of parameters poses significant challenges for practical deployment. Pruning, a technique aimed at…

Computation and Language · Computer Science 2024-12-16 Jiwon Song , Kyungseok Oh , Taesu Kim , Hyungjun Kim , Yulhwa Kim , Jae-Joon Kim

Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This paper investigates the effectiveness of these methods in…

Machine Learning · Computer Science 2024-05-30 Saswat Das , Marco Romanelli , Cuong Tran , Zarreen Reza , Bhavya Kailkhura , Ferdinando Fioretto

Recently, work in NLP has shifted to few-shot (in-context) learning, with large language models (LLMs) performing well across a range of tasks. However, while fairness evaluations have become a standard for supervised methods, little is…

Computation and Language · Computer Science 2023-11-16 Carlos Aguirre , Kuleen Sasse , Isabel Cachola , Mark Dredze

As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters. However, in this study, we discovered that many layers of…

Computation and Language · Computer Science 2024-10-14 Xin Men , Mingyu Xu , Qingyu Zhang , Bingning Wang , Hongyu Lin , Yaojie Lu , Xianpei Han , Weipeng Chen

Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical. Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's privacy awareness through Supervised Fine-Tuning (SFT)…

Artificial Intelligence · Computer Science 2025-06-04 Chen Qian , Dongrui Liu , Jie Zhang , Yong Liu , Jing Shao

Parameter Efficient Fine-Tuning (PEFT) has become the de-facto approach in adapting Large Language Models (LLMs) for downstream tasks in Natural Language Processing. However, its adoption in privacy-preserving distributed learning…

Computation and Language · Computer Science 2025-06-03 Yeshwanth Venkatesha , Souvik Kundu , Priyadarshini Panda

Transformer-based models have achieved dominant performance in numerous NLP tasks. Despite their remarkable successes, pre-trained transformers such as BERT suffer from a computationally expensive self-attention mechanism that interacts…

Computation and Language · Computer Science 2024-06-04 Jungmin Yun , Mihyeon Kim , Youngbin Kim

Pre-trained language models (PLMs) have revolutionized both the natural language processing research and applications. However, stereotypical biases (e.g., gender and racial discrimination) encoded in PLMs have raised negative ethical…

Computation and Language · Computer Science 2024-07-12 Jinfeng Li , Yuefeng Chen , Xiangyu Liu , Longtao Huang , Rong Zhang , Hui Xue

Recent work on pruning large language models (LLMs) has shown that one can eliminate a large number of parameters without compromising performance, making pruning a promising strategy to reduce LLM model size. Existing LLM pruning…

Machine Learning · Computer Science 2024-10-16 Haiquan Lu , Yefan Zhou , Shiwei Liu , Zhangyang Wang , Michael W. Mahoney , Yaoqing Yang

In this paper, we propose a rotation-constrained compensation method to address the errors introduced by structured pruning of large language models (LLMs). LLMs are trained on massive datasets and accumulate rich semantic knowledge in…

Computation and Language · Computer Science 2026-03-02 Shuichiro Haruta , Kazunori Matsumoto , Zhi Li , Yanan Wang , Mori Kurokawa

Large Language Models (LLMs) are being adopted across a wide range of tasks, including decision-making processes in industries where bias in AI systems is a significant concern. Recent research indicates that LLMs can harbor implicit biases…

Computation and Language · Computer Science 2024-10-18 Divyanshu Kumar , Umang Jain , Sahil Agarwal , Prashanth Harshangi

Transformer-based large language models (LLMs) excel in modeling complex language patterns but face significant computational costs during inference, especially with long inputs due to the attention mechanism's memory overhead. We observe…

Computation and Language · Computer Science 2024-10-18 Ruiqing Yan , Linghan Zheng , Xingbo Du , Han Zou , Yufeng Guo , Jianfei Yang

We study model pruning methods applied to Transformer-based neural network language models for automatic speech recognition. We explore three aspects of the pruning frame work, namely criterion, method and scheduler, analyzing their…

Machine Learning · Computer Science 2023-10-06 Leonardo Emili , Thiago Fraga-Silva , Ernest Pusateri , Markus Nußbaum-Thom , Youssef Oualil

Large language models (LLMs) are expensive to serve because model parameters, attention computation, and KV caches impose substantial memory and latency costs. We present GRASPrune, a structured pruning framework applied after pretraining…

Artificial Intelligence · Computer Science 2026-04-22 Ziyang Wang , Jiangfeng Xiao , Chuan Xiao , Ruoxiang Li , Rui Mao , Jianbin Qin

Layer pruning efficiently reduces Large Language Model (LLM) computational costs but often triggers sudden performance collapse. Existing representation-based analyses struggle to explain this mechanism. We propose studying pruning through…

Computation and Language · Computer Science 2026-05-11 Boyu Shi , Chang Liu , ChuanBao Gao , Xu Yang , Xin Geng