FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression (2409.17141v1)

Abstract: While the LLMing objective has been shown to be deeply connected with compression, it is surprising that modern LLMs are not employed in practical text compression systems. In this paper, we provide an in-depth analysis of neural network and transformer-based compression techniques to answer this question. We compare traditional text compression systems with neural network and LLM-based text compression methods. Although LLM-based systems significantly outperform conventional compression methods, they are highly impractical. Specifically, LLMZip, a recent text compression system using Llama3-8B requires 9.5 days to compress just 10 MB of text, although with huge improvements in compression ratios. To overcome this, we present FineZip - a novel LLM-based text compression system that combines ideas of online memorization and dynamic context to reduce the compression time immensely. FineZip can compress the above corpus in approximately 4 hours compared to 9.5 days, a 54 times improvement over LLMZip and comparable performance. FineZip outperforms traditional algorithmic compression methods with a large margin, improving compression ratios by approximately 50\%. With this work, we take the first step towards making lossless text compression with LLMs a reality. While FineZip presents a significant step in that direction, LLMs are still not a viable solution for large-scale text compression. We hope our work paves the way for future research and innovation to solve this problem.

• The paper introduces FineZip, a hybrid online/offline LLM approach that significantly improves lossless text compression efficiency.
• It demonstrates a 54× reduction in compression time by processing a 10MB corpus in about four hours compared to 9.5 days with previous methods.
• The study shows that while FineZip outperforms traditional algorithms with a roughly 50% better compression ratio, further refinements are needed for practical large-scale deployment.

Pushing the Limits of Lossless Text Compression with FineZip

The paper presents FineZip, an innovative approach leveraging LLMs for lossless text compression. The work builds upon the existing understanding that LLMing inherently aligns with data compression, a concept explored by various researchers over the decades. Previous methodologies, including those by Schmidhuber and Heil (1996) and modern LLM-based systems like LLMZip, have pursued this alignment. However, FineZip introduces a distinctive framework that significantly enhances compression efficiency.

FineZip takes a hybrid approach that amalgamates "online" and "offline" components. The online component involves fine-tuning the LLM on corpus-specific data, a task accomplished in a parameter-efficient manner using techniques like LoRA (Hu et al., 2021). This step acts as a memorization phase, prepping the model for the subsequent compression task. The offline component maintains a fixed pre-trained LLM, which ensures a stable baseline for compression across different datasets. By introducing a dynamic context, where the context size adapts to each token's position, FineZip can batch process, thus enhancing compression speed without demanding excessive computational resources.

The experimental analysis highlighted in the paper indicates that FineZip achieves a compression time improvement by a factor of 54 over its predecessor, LLMZip, while maintaining comparable compression ratios. Specifically, FineZip can compress a standard 10MB text corpus in approximately four hours, compared to the 9.5 days required by LLMZip. Moreover, it outperforms traditional algorithms like gzip and bzip2 by enhancing compression ratios by approximately 50%.

Key results from the experiments show that FineZip's compression ratio outmatches that of traditional methods while sharply reducing the time for compression compared to other LLM-based solutions like LLMZip. However, despite these advances, the absolute compression rates achieved through LLM-based methods remain insufficient for broad-scale practical application. The implementation of FineZip brings LLM-based compression closer to real-world use but indicates that further refinement is necessary to streamline compression times and memory efficiency fully.

The development of FineZip emphasizes the significant potential of LLMs not just in understanding language but in practical tasks like text compression. It underscores the dual benefits of leveraging sophisticated predictive capabilities in LLMs while also employing strategic fine-tuning and context adaptation techniques. Future AI developments might see more personalized compression applications as computational resources evolve, allowing mobile and personal devices to execute such sophisticated models more feasibly.

Conclusively, while FineZip makes notable strides in reducing the computational burden of LLM-based compression, the research suggests an ongoing need to address the impracticality of current LLM-based methods for real-world deployment. The findings of this research stimulate further exploration in making LLMs more adaptable and efficient for practical compression scenarios. As LLM capabilities and computing power advance, these advancements might pave the way for LLM-based systems that balance performance with practicality in text compression tasks.