Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks

• URL: http://arxiv.org/abs/2405.11704v1
• Date: Mon, 20 May 2024 00:10:00 GMT
• Title: Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks
• Authors: Taiyuan Mei, Yun Zi, Xiaohan Cheng, Zijun Gao, Qi Wang, Haowei Yang,
• Abstract summary: Internal structure and operation mechanism of large-scale language models are analyzed theoretically. We evaluate the contribution of adaptive optimization algorithms (such as AdamW), massively parallel computing techniques, and mixed precision training strategies.
• Score: 6.596361762662328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The internal structure and operation mechanism of large-scale language models are analyzed theoretically, especially how Transformer and its derivative architectures can restrict computing efficiency while capturing long-term dependencies. Further, we dig deep into the efficiency bottleneck of the training phase, and evaluate in detail the contribution of adaptive optimization algorithms (such as AdamW), massively parallel computing techniques, and mixed precision training strategies to accelerate convergence and reduce memory footprint. By analyzing the mathematical principles and implementation details of these algorithms, we reveal how they effectively improve training efficiency in practice. In terms of model deployment and inference optimization, this paper systematically reviews the latest advances in model compression techniques, focusing on strategies such as quantification, pruning, and knowledge distillation. By comparing the theoretical frameworks of these techniques and their effects in different application scenarios, we demonstrate their ability to significantly reduce model size and inference delay while maintaining model prediction accuracy. In addition, this paper critically examines the limitations of current efficiency optimization methods, such as the increased risk of overfitting, the control of performance loss after compression, and the problem of algorithm generality, and proposes some prospects for future research. In conclusion, this study provides a comprehensive theoretical framework for understanding the efficiency optimization of large-scale language models.

Related papers

• Transformer^-1: Input-Adaptive Computation for Resource-Constrained Deployment [3.6219999155937113]
  This paper proposes a Transformer$-1$ architecture to address the resource waste caused by fixed computation paradigms in deep learning models under dynamic scenarios. In a benchmark test, our method reduces FLOPs by 42.7% and peak memory usage by 3% compared to the standard Transformer. We also conducted experiments on several natural language processing tasks and achieved significant improvements in resource efficiency.
  arXiv  Detail & Related papers  (2025-01-26T15:31:45Z)
• Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models [4.737806982257592]
  This study proposes a knowledge distillation algorithm based on large language models and feature alignment. The proposed model performs very close to the state-of-the-art GPT-4 model in terms of evaluation indicators such as perplexity, BLEU, ROUGE, and CER.
  arXiv  Detail & Related papers  (2024-12-27T04:37:06Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Numerical Pruning for Efficient Autoregressive Models [87.56342118369123]
  This paper focuses on compressing decoder-only transformer-based autoregressive models through structural weight pruning. Specifically, we propose a training-free pruning method that calculates a numerical score with Newton's method for the Attention and modules, respectively. To verify the effectiveness of our method, we provide both theoretical support and extensive experiments.
  arXiv  Detail & Related papers  (2024-12-17T01:09:23Z)
• Randomized Dimension Reduction with Statistical Guarantees [0.27195102129095]
  This thesis explores some of such algorithms for fast execution and efficient data utilization. We focus on learning algorithms with various incorporations of data augmentation that improve generalization and distributional provably. Specifically, Chapter 4 presents a sample complexity analysis for data augmentation consistency regularization.
  arXiv  Detail & Related papers  (2023-10-03T02:01:39Z)
• Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
  We take a closer theoretical look at Independent Subnetwork Training (IST) IST is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
  arXiv  Detail & Related papers  (2023-06-28T18:14:22Z)
• Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant [18.592094066642364]
  This article provides a comprehensive understanding of optimization in deep learning. We focus on the challenges of gradient vanishing and gradient exploding, which normally lead to diminished model representational ability and training instability, respectively. To help understand the current optimization methodologies, we categorize them into two classes: explicit optimization and implicit optimization.
  arXiv  Detail & Related papers  (2023-06-15T17:59:27Z)
• Towards Compute-Optimal Transfer Learning [82.88829463290041]
  We argue that zero-shot structured pruning of pretrained models allows them to increase compute efficiency with minimal reduction in performance. Our results show that pruning convolutional filters of pretrained models can lead to more than 20% performance improvement in low computational regimes.
  arXiv  Detail & Related papers  (2023-04-25T21:49:09Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
  We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings. We exploit a warm-start strategy to amortize the estimation of the exact gradient. By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
  arXiv  Detail & Related papers  (2021-11-29T15:10:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.