Applying LLM-based compression techniques like FineZip to large-scale, real-world text corpora presents several challenges and limitations. One significant challenge is the computational cost associated with using large language models (LLMs). The memory and processing power required for both fine-tuning and compression can be prohibitive, especially for organizations without access to high-performance computing resources. This limits the practicality of deploying such systems in environments with constrained resources.
Another limitation is the scalability of LLMs. While FineZip demonstrates improved compression ratios, the time taken for compression remains a concern, particularly for massive datasets. The current implementation, although significantly faster than previous methods, may still be too slow for real-time applications or scenarios requiring rapid data processing.
Moreover, LLMs may struggle with domain-specific language or highly specialized text, leading to suboptimal compression ratios. The effectiveness of FineZip relies on the model's ability to understand and predict text patterns, which may not be as robust in niche areas compared to more generalized text. Additionally, the dynamic context approach, while beneficial for certain datasets, may introduce variability in performance depending on the structure and nature of the text being compressed.
Lastly, the reliance on GPU availability poses a limitation, as not all users or systems are equipped with the necessary hardware to run LLMs locally. This could hinder widespread adoption and limit the accessibility of LLM-based compression techniques.