Summary of Stacking Your Transformers: a Closer Look at Model Growth For Efficient Llm Pre-training, by Wenyu Du et al.
Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training
by Wenyu Du, Tongxu Luo, Zihan Qiu, Zeyu Huang, Yikang Shen, Reynold Cheng, Yike Guo, Jie Fu
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 introduces a new approach to efficiently pre-training large language models (LLMs) using smaller models as a growth accelerator. The authors identify three key obstacles in existing methods: lack of comprehensive evaluation, untested viability for scaling, and lack of empirical guidelines. To address these challenges, the study summarizes four atomic growth operators and evaluates them in a standardized LLM pre-training setting. The results show that one operator, called G_{}, significantly accelerates training while improving performance on eight standard NLP benchmarks. The paper also explores the scalability of G_{} up to 7B LLMs and provides guidelines for determining growth timing and factor, making it practical for general LLM pre-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make large language models more efficient by using smaller models as a growth accelerator. Researchers have been trying to find ways to train these big models faster without sacrificing their quality. The authors identified three main problems with current methods: they don’t test them well, they’re not tested at different scales, and there aren’t any clear guidelines for how to use them. To solve these issues, the study groups four basic building blocks of growth together and tests them all in a consistent way. One of these blocks, called G_{}, is really good at making training faster while keeping performance high on many natural language processing tasks. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
