SemanticZip: A Pilot Framework for Lossy Text Compression with LLMs as Semantic Decompressors
arXiv:2605.24541v1 Announce Type: cross Abstract: Text compression for large language model (LLM) systems is usually framed as token deletion, retrieval, summarization, or exact reconstruction. We study a more aggressive but explicitly lossy setting: compress text into compact codes that an LLM can expand into task-relevant meaning. We call this setting SemanticZip. Unlike lossless compression, SemanticZip does not require byte-identical reconstruction; unlike ordinary summarization, it treats model-based decompression as part of the codec and evaluates whether task-relevant semantic commitmen
The proliferation of Large Language Models (LLMs) and the increasing demand for efficient data handling push the boundaries of traditional compression, making innovative solutions like semantic compression timely.
This development could significantly reduce storage and computational costs associated with LLM-generated or LLM-processed text, impacting the scalability and deployment of AI systems.
The paradigm shifts from exact reconstruction in text compression to a lossy, semantics-focused approach where LLMs act as decompressors, prioritizing meaning over byte-level fidelity.
- · AI platform providers
- · Cloud infrastructure providers
- · Data storage companies
- · LLM developers
- · Companies reliant on traditional lossless compression for text
- · Legacy data management systems
Reduced operational costs for LLM deployments due to more efficient data handling.
Faster and more widespread adoption of complex AI applications as data bottlenecks are eased.
New forms of data transmission and storage emerging, optimized for AI interpretation rather than human readability or exact reconstruction.
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