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

arXiv: https://arxiv.org/abs/2405.11704 · HTML: https://mlsanity.com/paper/2405.11704/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://mlsanity.com/paper/2004.02984/) (2020)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN](https://mlsanity.com/paper/2308.08333/) (2023)
- [Regional Style and Color Transfer](https://mlsanity.com/paper/2404.13880/) (2024)
- [A Review of Winograd Schema Challenge Datasets and Approaches](https://mlsanity.com/paper/2004.13831/) (2020)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis](https://mlsanity.com/paper/2404.08003/) (2024)
- [Constrained Attractor Selection Using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.10500/) (2019)
- [Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification](https://mlsanity.com/paper/2404.14606/) (2024)
- [Utilizing Deep Learning to Optimize Software Development Processes](https://mlsanity.com/paper/2404.13630/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Text Classification of Cancer Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2309.07812/) (2023)
- [Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases](https://mlsanity.com/paper/2403.17135/) (2024)

## Built on by (most cited first, 14 of 14)

- [Attention Mechanism and Context Modeling System for Text Mining Machine Translation](https://mlsanity.com/paper/2408.04216/) (2024)
- [A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods](https://mlsanity.com/paper/2408.03568/) (2024)
- [Advanced Financial Fraud Detection Using GNN-CL Model](https://mlsanity.com/paper/2407.06529/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment](https://mlsanity.com/paper/2409.17909/) (2024)
- [Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines](https://mlsanity.com/paper/2406.13626/) (2024)
- [Text classification optimization algorithm based on graph neural network](https://mlsanity.com/paper/2408.15257/) (2024)
- [Exploiting Diffusion Prior for Out-of-Distribution Detection](https://mlsanity.com/paper/2406.11105/) (2024)
- [Reducing Bias in Deep Learning Optimization: The RSGDM Approach](https://mlsanity.com/paper/2409.15314/) (2024)
- [A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems](https://mlsanity.com/paper/2407.01712/) (2024)
- [Stock Type Prediction Model Based on Hierarchical Graph Neural Network](https://mlsanity.com/paper/2412.06862/) (2024)
- [Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective](https://mlsanity.com/paper/2406.13152/) (2024)
- [Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability](https://mlsanity.com/paper/2504.16056/) (2025)
- [TaskComplexity: A Dataset for Task Complexity Classification with In-Context Learning, FLAN-T5 and GPT-4o Benchmarks](https://mlsanity.com/paper/2409.20189/) (2024)

## Abstract (arXiv)

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.