LLM Codeword Representation for Lower-Cost Training and Inference
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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 system and method for training and operating large language models using codewords, which are discrete, compressed representations that capture the inherent structure and patterns of data, allowing for efficient processing and generation across various data modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If dense embeddings are used to represent tokens, then semantic and syntactic information is captured, but computational cost and memory usage increase significantly
Solution Approach 1:
The patent segments the continuous dense embedding space into discrete codeword clusters. Each cluster represents a group of semantically similar tokens, and is represented by a single codeword vector. This segmentation reduces the computational burden by transforming continuous vector operations into discrete index lookups, while preserving semantic information through the clustered representation.
Solution Approach 2:
The patent changes the parameter representation from continuous dense vectors to discrete codeword indices. Instead of operating with high-dimensional continuous embedding vectors, the system operates with discrete integer indices that point to pre-computed codeword representations. This parameter transformation dramatically reduces memory bandwidth requirements and computational complexity while maintaining semantic fidelity through the clustered codebook structure.
2Adaptability or versatility
If dense embeddings are used for large vocabularies, then comprehensive token representation is achieved, but memory intensity increases
Solution Approach 1:
The patent creates a compressed copy of the embedding space in the form of a codebook. Instead of storing and processing full dense embeddings for all tokens in the vocabulary, the system stores a compact codebook where each entry represents a cluster of semantically similar tokens. During inference, the system copies only the necessary codeword indices from the codebook, dramatically reducing memory usage while maintaining comprehensive token representation coverage.
3Manufacturing precision
If learned representations are specific to language and domain, then training accuracy improves, but generalization ability decreases
Solution Approach 1:
The patent creates a universal codebook structure that can serve multiple languages and domains simultaneously. The codeword clusters are organized in a hierarchical manner where lower-level clusters capture language-specific patterns while higher-level clusters capture domain-agnostic semantic concepts. This universal structure allows the model to generalize across languages and domains by operating at appropriate levels of the hierarchy, rather than being locked into language-specific representations.
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.