Summary of Patch-level Training For Large Language Models, by Chenze Shao et al.
Patch-Level Training for Large Language Models
by Chenze Shao, Fandong Meng, Jie Zhou
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper addresses the efficiency concerns of Large Language Models (LLMs) by introducing patch-level training, a method that reduces computational costs without compromising model performance. Traditionally, LLMs are trained to predict the next token in a sequence, but this approach requires processing an extensive number of tokens, leading to high computational costs. To mitigate this issue, the authors propose patch-level training, which compresses multiple tokens into a single patch and trains the language model to predict the next patch. This approach reduces the sequence length and thus computational costs by 0.5 times compared to token-level training. The authors demonstrate the effectiveness of patch-level training on various models (370M-2.7B parameters) without sacrificing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at understanding and generating language. Currently, these machines are really good at predicting what comes next in a sentence or text. However, it takes a lot of computer power to do this. The authors came up with a new way to train these language models that uses smaller chunks of text instead of the whole sentence. This makes their training faster and more efficient without losing any quality. They tested this method on several different language models and found it works just as well as the old way, but much faster. |
Keywords
* Artificial intelligence * Language model * Token
