Summary of Multitok: Variable-length Tokenization For Efficient Llms Adapted From Lzw Compression, by Noel Elias et al.
MultiTok: Variable-Length Tokenization for Efficient LLMs Adapted from LZW Compression
by Noel Elias, Homa Esfahanizadeh, Kaan Kale, Sriram Vishwanath, Muriel Medard
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG)
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 proposed novel tokenization method, MultiTok, inspired by universal Lempel-Ziv-Welch data compression, compresses repetitive phrases into multi-word tokens. This enables the efficient training of large language models (LLMs) while maintaining similar accuracy on compressed training data. The results demonstrate that MultiTok achieves comparable performance to BERT and GPT-2 as both a standalone tokenizer and an add-on to existing tokenizers, offering 2.5x faster training with over 30% less training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can process complex natural language tasks more efficiently. However, current methods for training these LLMs require many resources, like lots of data, expensive equipment, and long training periods. To solve this problem, researchers propose a new way to break down text into smaller parts called tokens. This method is inspired by how we compress files on our computers. By using this new tokenization technique, called MultiTok, the team shows that language models can be trained more quickly while still producing similar results. They even compare their approach to two popular methods, BERT and GPT-2, and find that it works just as well. |
Keywords
» Artificial intelligence » Bert » Gpt » Tokenization » Tokenizer
