Summary of Simple and Scalable Strategies to Continually Pre-train Large Language Models, by Adam Ibrahim et al.
Simple and Scalable Strategies to Continually Pre-train Large Language Models
by Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents an efficient approach to continually pre-training large language models (LLMs) on new data while maintaining performance on previous data. The authors show that a combination of learning rate re-warming, re-decaying, and replaying previous data is sufficient to match the performance of fully re-training from scratch. This approach saves significant compute resources compared to re-training from scratch. The paper demonstrates this method for weak and strong distribution shifts between English and German datasets at different scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, the paper finds an innovative way to keep large language models updated without wasting too much computer power. Instead of starting over again when new data becomes available, the authors show that simple techniques can help the model learn from both old and new data simultaneously. This is important because it makes machine learning more efficient. |
Keywords
* Artificial intelligence * Machine learning
