Summary of Finezip : Pushing the Limits Of Large Language Models For Practical Lossless Text Compression, by Fazal Mittu et al.
FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression
by Fazal Mittu, Yihuan Bu, Akshat Gupta, Ashok Devireddy, Alp Eren Ozdarendeli, Anant Singh, Gopala Anumanchipalli
First submitted to arxiv on: 25 Sep 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 explores the connection between language modeling objectives and compression techniques in neural networks and transformers. The authors compare traditional text compression systems with recent LLM-based methods, which significantly outperform conventional approaches but are impractical due to long compression times. To address this issue, the researchers introduce FineZip, a novel LLM-based system that combines online memorization and dynamic context to reduce compression time. FineZip achieves a 54-time improvement over previous methods, matching performance while compressing text in approximately 4 hours. The authors conclude that FineZip outperforms traditional algorithmic compression methods by around 50% and takes the first step towards making lossless text compression with LLMs feasible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can squeeze data to make it smaller, which is important for storing and sharing information. The authors are trying to figure out why modern language models aren’t being used in this process yet. They compare old methods with new ones that use language models and find that the new methods work much better, but they take too long to compress text. To fix this problem, they created a new method called FineZip that is faster and still works well. FineZip can shrink data by 50% more than other methods and takes about 4 hours to do it, which is much faster than before. The authors think this step forward will help make it possible for computers to use language models for compressing text. |
