LLM Codeword Representation for Lower-Cost Language Processing
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Solution Overview
Problem
Current deep learning models operate on dense embeddings, which are computationally expensive and memory-intensive, especially for large vocabularies, and are limited in their ability to generalize across languages and domains.
Innovation Solution
A large codeword model (LCM) that processes discrete, compressed representations called codewords, using architectures like Transformers or Variational Autoencoders, to capture underlying patterns and relationships in data efficiently, enabling seamless transfer learning across different domains and modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If dense embeddings are used to represent tokens in deep learning models, then semantic and syntactic information is captured, but computational cost and memory usage increase significantly
Solution Approach 1:
The patent extracts only the essential semantic information from tokens and represents it using compact codewords instead of full dense embeddings. This extraction approach maintains the necessary semantic and syntactic information while significantly reducing the computational burden associated with processing large-dimensional embedding vectors.
Solution Approach 2:
Instead of using dense embeddings to represent tokens and then compressing them, the patent inverts the approach by directly encoding tokens into compact codewords that preserve essential information. This inversion eliminates the need for computationally expensive dense embedding operations while maintaining information quality.
2Loss of information
If dense embeddings are used for large vocabularies, then comprehensive token representation is achieved, but memory requirements become prohibitive
Solution Approach 1:
The patent extracts only the critical semantic features from tokens and stores them as compact codewords. This extraction eliminates the need to store complete dense embeddings for every token in the vocabulary, dramatically reducing memory requirements while preserving essential representation quality.
Solution Approach 2:
The patent replaces expensive, large-memory dense embeddings with cheap, compact codewords. These codewords serve as disposable representations that can be easily regenerated or recomputed from the original tokens when needed, eliminating the need for persistent storage of large embedding matrices.
3Measurement precision
If token-based approaches with dense embeddings are used, then language-specific representations are learned, but generalization to new languages and domains is limited
Solution Approach 1:
The patent creates a universal codeword representation system that can represent tokens from any language or domain using the same compact encoding framework. This universal approach allows the model to learn domain-specific patterns while maintaining the ability to generalize across different languages and domains, unlike language-specific dense embeddings.
Solution Approach 2:
The patent changes the fundamental parameter of representation from high-dimensional dense embeddings to low-dimensional codewords. This parameter change enables the model to capture essential semantic information with fewer dimensions, improving generalization capability while maintaining representation accuracy across different languages and domains.
4Loss of information
If traditional token-based processing is used, then detailed text analysis is possible, but processing efficiency decreases
Solution Approach 1:
The patent segments text into tokens and then further compresses each token into a compact codeword representation. This segmentation approach maintains the detailed structure of text while reducing the processing burden of each individual token, thereby improving overall processing efficiency without losing essential text information.
Solution Approach 2:
Instead of processing detailed token representations and then summarizing, the patent inverts the approach by directly converting tokens to compact codewords that preserve essential information. This inversion enables efficient processing while maintaining text detail information, reversing the traditional inefficient workflow.
Data Source
AI summary
This invention presents an optimized approach for training and operating Large Language Models (LLMs) using codewords. By converting traditional token-based LLMs to codeword-based systems, the method achieves significant efficiency gains. The process involves tokenizing training data and assigning codewords to tokens. LLMs are then trained and operated using these compact codewords instead of conventional tokens. During operation, prompts are converted to codewords, processed by the LLM, and the outputs are converted back to text. This approach reduces the overall cost of training and operating LLMs by approximately, offering a more efficient solution for large-scale language processing tasks.