Language Model Compression Using Vectorized Data Representations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data compression and decompression technologies face challenges in efficiently and effectively utilizing language models to improve compression and decompression quality and efficiency across various data modalities.
Innovation Solution
Implement data compression and decompression methods using language models, generating input sequences based on prompts and extracting vectorized representations of target data, and employing supervised fine-tuning to enhance accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional data compression algorithms are used, then data size is reduced, but compression quality and restoration accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a language model-based system that uses semantic understanding to achieve compression. The language model captures the meaning and structure of the data, enabling high-quality restoration while maintaining compression efficiency.
Solution Approach 2:
The patent changes the fundamental parameter of compression from bit-level manipulation to semantic-level representation. By using language models to understand and represent data meaning, the system achieves better compression quality without sacrificing restoration accuracy.
2Manufacturing precision
If language models are used for data compression, then compression quality improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the compression process into distinct stages: input processing, language model transformation, and output generation. This segmentation allows for optimized processing at each stage and enables parallel computation where applicable, reducing overall computational complexity.
Solution Approach 2:
The patent performs preliminary processing of the input data before feeding it to the language model, including tokenization and formatting. This preliminary action prepares the data in an optimal format for the language model, reducing the computational burden during the main compression process.
3Manufacturing precision
If language models are used for data compression, then restoration accuracy improves, but processing speed decreases
Solution Approach 1:
The patent employs periodic action through the use of token-based processing and iterative generation. The language model processes data in discrete token units and generates output iteratively, allowing for parallel processing of multiple tokens and optimization of processing speed while maintaining high restoration accuracy.
4Speed
If traditional compression methods are used, then processing speed is maintained, but adaptability to different data modalities deteriorates
Solution Approach 1:
The patent implements universality by designing a language model-based compression system that can handle multiple data modalities (text, code, structured data) through a unified framework. The language model's ability to understand and process different types of data enables high adaptability while maintaining efficient processing speeds.
Data Source
AI summary
Embodiments of the disclosure provide a method, an apparatus, a device and a readable medium for data compression and decompression. A method for data compression includes: generating a first input sequence for a first target model based on a first prompt and target data to be compressed, the first target model being constructed based on a language model, the first prompt indicating the first target model to perform a data compression task; obtaining a first output sequence of the first target model by providing the first input sequence to the first target model; and extracting a compressed representation of the target data from the first output sequence, the compressed representation being a vectorized representation of the target data.