arXiv:2604.02343v2 Announce Type: replace-cross Abstract: We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is possible at the cost of more compute. For lossless compression, domain-adapted LoRA adapters can improve LLM-based arithmetic coding by 2x over compression with the base LLM alone. For lossy compression, prompting a model for a succinct rewrite then applying arithmetic coding can achieve compression ratios of approximately 0.03, a 2x improvement over compressing the original response.
Ongoing advancements in LLM capabilities and the increasing need for efficient data management drive research into innovative compression techniques.
Improved compression of LLM-generated text can significantly reduce storage, transmission costs, and computational overhead for AI systems.
New methods leveraging LLMs for both lossless and lossy text compression offer substantial gains, potentially reshaping how large language model outputs are handled.
- · Cloud providers
- · AI developers
- · Any industry relying on large text data
- · Traditional text compression algorithms (relatively)
Significant reduction in data storage and transmission costs for LLM applications.
Enabling more complex and larger-scale LLM deployments due to lower infrastructure demands.
Potentially democratizing access to powerful LLM technologies by lowering their operational costs.
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Read at arXiv cs.AI