Summary of Data Engineering For Scaling Language Models to 128k Context, by Yao Fu et al.
Data Engineering for Scaling Language Models to 128K Context
by Yao Fu, Rameswar Panda, Xinyao Niu, Xiang Yue, Hannaneh Hajishirzi, Yoon Kim, Hao Peng
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 A new study on scaling language models’ context lengths to 128,000 tokens explores the benefits of continual pretraining on large datasets. The researchers hypothesize that large-scale pretraining enables models to acquire the ability to utilize information at arbitrary input locations, which can be extended to longer contexts through lightweight continual pretraining. They investigate the quantity and quality of data needed for this approach, finding that 500 million to 5 billion tokens are sufficient, but domain balance and length upsampling are crucial. The study demonstrates an effective strategy for scaling context lengths, outperforming strong open-source models and closing the gap to frontier models like GPT-4 128K. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers looked at how to make language models better at understanding very long pieces of text. They thought that by training the model on a lot of data, it would get good at using information from anywhere in the text. To test this idea, they tried training the model on different amounts and types of data. They found that giving the model 500 million to 5 billion tokens was enough for it to work well, but it’s also important to make sure the data is a mix of different kinds of texts. This new approach helps language models understand longer pieces of text and performs better than other models. |
Keywords
» Artificial intelligence » Gpt » Pretraining
