Codeword-Based LLM Processing for Lower Memory and Compute
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Solution Overview
Problem
Existing 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 data using discrete, compressed codewords, capturing inherent structure and patterns, allowing for efficient processing and generation across various data modalities, including text, images, and audio, and enabling transfer learning across domains.
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. This segmentation reduces the infinite continuous space into a manageable discrete vocabulary, lowering computational cost while preserving semantic information through cluster centroids.
Solution Approach 2:
The patent changes the parameter representation from continuous dense vectors to discrete codewords. By transforming the embedding space into a coded representation where each codeword corresponds to a cluster of semantically related tokens, the system reduces memory requirements and computational complexity while maintaining semantic fidelity through the cluster-based organization.
2Adaptability or versatility
If large vocabularies are used to cover diverse languages and domains, then model versatility improves, but memory requirements and processing complexity increase
Solution Approach 1:
The patent creates a universal codeword representation that can represent tokens from multiple languages and domains. Instead of maintaining separate embeddings for each language or domain, the codeword system provides a unified discrete representation that captures cross-lingual and cross-domain patterns, enabling the model to handle diverse data with a single versatile vocabulary.
Solution Approach 2:
The patent combines multiple language and domain-specific token distributions into a composite codeword vocabulary. By aggregating token frequencies and semantic patterns across diverse datasets during codeword generation, the system creates a unified representation space that efficiently encodes information from multiple sources without requiring separate memory allocations for each language or domain.
3Loss of information
If tokenization is performed at fine granularity to preserve detail, then information retention improves, but processing complexity and computational load increase
Solution Approach 1:
The patent applies partial action by tokenizing only the necessary level of detail required for each specific task. Rather than consistently using fine-grained tokenization across all inputs, the codeword system allows dynamic adjustment of tokenization granularity based on the complexity and requirements of the input data, reducing processing complexity while preserving essential information.
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.