Summary of Efficiency Optimization Of Large-scale Language Models Based on Deep Learning in Natural Language Processing Tasks, by Taiyuan Mei et al.
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks
by Taiyuan Mei, Yun Zi, Xiaohan Cheng, Zijun Gao, Qi Wang, Haowei Yang
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper analyzes the internal structure and operation mechanism of large-scale language models, specifically exploring how Transformer architectures can balance computing efficiency with long-term dependency capture. It also delves into the training phase’s efficiency bottleneck, evaluating adaptive optimization algorithms like AdamW, massively parallel computing techniques, and mixed precision training strategies that accelerate convergence and reduce memory footprint. The authors reveal how these algorithms improve training efficiency in practice by analyzing mathematical principles and implementation details. Additionally, the paper reviews model compression techniques like quantification, pruning, and knowledge distillation, comparing their effects on different application scenarios to demonstrate reduced model size and inference delay while maintaining prediction accuracy. Finally, it examines current efficiency optimization methods’ limitations and proposes future research prospects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language models work and why they can be slow or use too much memory. The researchers want to understand what makes these models efficient or inefficient and find ways to make them faster and more accurate. They look at special algorithms that help train the models, like AdamW, and new ways of doing math that speed up calculations. They also explore ways to shrink big models into smaller ones without losing their accuracy. The study concludes by highlighting what’s currently working well and what still needs to be improved. |
Keywords
» Artificial intelligence » Inference » Knowledge distillation » Model compression » Optimization » Precision » Pruning » Transformer
