Efficient Storage and Compression for Large Language Models

The field of natural language processing is moving towards more efficient storage and compression methods for large language models (LLMs). Researchers are exploring innovative techniques such as tensor deduplication, delta compression, and lossless compression algorithms to reduce storage consumption and improve data management. These advancements have the potential to significantly reduce the costs associated with storing and processing LLMs, making them more accessible and efficient for a wide range of applications. Notably, some papers have proposed novel architectures and methods that achieve state-of-the-art performance in tasks such as text compression and graph signal processing.

Some noteworthy papers include:

  • Towards Efficient LLM Storage Reduction via Tensor Deduplication and Delta Compression, which presents a effective storage reduction pipeline that reduces model storage consumption by 49.5 percent.
  • Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction, which achieves remarkable compression rates exceeding 20x through LLM-based prediction methods.
  • Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain, which proposes a novel method that interprets self-attention as learning the graph filter in the singular value domain and achieves state-of-the-art performance on various tasks.

Sources

Towards Efficient LLM Storage Reduction via Tensor Deduplication and Delta Compression

Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction

Graph Laplacian Wavelet Transformer via Learnable Spectral Decomposition

HYPERNYM MERCURY: Token Optimization through Semantic Field Constriction and Reconstruction from Hypernyms. A New Text Compression Method

Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding

Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain

Lossless Compression for LLM Tensor Incremental Snapshots

Learning Kronecker-Structured Graphs from Smooth Signals

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