Although the connection between language modeling and data compression has been recognized for some time, current Large Language Models (LLMs) are not typically used for practical text compression due to their lengthy processing times. For example, LLMZip, a recent compression system based on the LLaMA3-8B model, requires 9.5 days to compress just 10 MB of data.
In a new paper FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression, a research team from UC Berkeley and NYU introduces FineZip, a novel LLM-based compression system designed to significantly reduce compression time. By incorporating techniques like online memorization and dynamic context adaptation, FineZip marks an important step toward the practical use of LLMs in lossless text compression.
FineZip’s architecture blends both online and offline elements. Its “online” component employs data-specific fine-tuning to efficiently memorize the content being compressed, while the “offline” portion uses pre-trained LLMs that remain static across different datasets. This dual approach enables the system to enhance performance through real-time adaptations without requiring extra storage for model parameters.
The online memorization leverages parameter-efficient fine-tuning (PEFT) techniques, adding only a small, fixed overhead via learned embeddings during compression. Meanwhile, the pre-trained LLM operates as a stable backbone, offering flexibility and consistency across various text corpora. This combination allows FineZip to benefit from online learning without suffering the resource burdens of traditional LLM-based methods.
A key innovation in FineZip is its use of dynamic context, where the context size for each token matches its position in a sentence. This approach enables more efficient batching during compression and decompression, leading to significant speed improvements.
By applying PEFT methods in the online phase, FineZip compensates for the performance drop caused by the dynamic context. At the same time, dynamic context facilitates parallel processing, allowing the system to handle multiple batches of text concurrently within a fixed computational budget.
Experimental results reveal that FineZip compresses the same dataset in approximately four hours, compared to the 9.5 days required by LLMZip—a 54x improvement in speed. Additionally, FineZip demonstrates a 50% enhancement in compression ratios over traditional algorithmic methods, signaling a major advance in the field. This work represents a crucial step toward making LLMs viable for efficient, lossless text compression.
The paper FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression is on arXiv.
Author: Hecate He | Editor: Chain Zhang
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FineZip showcases a groundbreaking leap in LLM-based lossless text compression, achieving remarkable speed and efficiency. Its innovative architecture and dynamic context adaptation set new standards. For more cutting-edge tech insights, visit Tap Drift—your gateway to technological excellence!