Summary of Adaptive Optimization For Enhanced Efficiency in Large-scale Language Model Training, by Jiajing Chen et al.
Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training
by Jiajing Chen, Bingying Liu, Xiaoxuan Liao, Jia Gao, Hongye Zheng, Yue Li
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed method uses an adaptive optimization algorithm to improve the efficiency and performance of large-scale language models (LLM). By comparing the algorithm with traditional methods like SGD, Momentum, AdaGrad, RMSProp, and Adam on SQuAD and GLUE datasets, it achieves better accuracy and F1 scores. The results show significant improvements, especially when processing large-scale texts and complex tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method is developed to help large language models learn more efficiently. This method uses an “adaptive optimization algorithm” to improve how the model works and how well it does. Tests are run on two different datasets (SQuAD and GLUE) to see how this new method compares to older methods like SGD, Momentum, and others. The results show that this new method is better at getting the right answers. |
Keywords
» Artificial intelligence » Optimization
