Word Hash Language Model Computational Efficiency
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Solution Overview
Problem
Existing language models are computationally intensive and require large resources due to their need to compute probabilities for each word in a large vocabulary, making them inefficient for applications like speech recognition and machine translation.
Innovation Solution
The implementation of statistical language models that output a vector describing likely next words instead of computing probabilities for each word, using word embeddings or hash vectors to reduce computational burden and resource requirements, allowing for more efficient processing on devices like smartphones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional language models compute probabilities for each word in a large vocabulary, then measurement precision of word likelihood is improved, but computational complexity and resource usage increase significantly
Solution Approach 1:
The patent extracts and pre-computes only the essential features of word representations (hash vectors) that are needed for language modeling, rather than computing full probability distributions over the entire vocabulary. This extraction approach maintains measurement precision for the most important aspects while eliminating unnecessary computational complexity.
Solution Approach 2:
The patent changes the parameter representation from full vocabulary probability distributions to compact hash vectors with fixed dimensions. This parameter transformation reduces the computational burden from O(vocabulary_size) to O(hash_vector_dimension), resolving the contradiction between precision and complexity.
2Reliability
If large language models with many parameters are used, then language modeling accuracy is improved, but memory usage and processing speed deteriorate
Solution Approach 1:
The patent uses hash vectors as compact copies or representations of full word embeddings, maintaining the essential semantic information needed for accurate language modeling while using significantly less memory. This copying approach enables faster processing by working with smaller data structures that preserve the core linguistic patterns.
3Measurement precision
If full vocabulary probability computation is performed, then word selection accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent performs partial computation by calculating hash vectors for only the most relevant word candidates rather than computing probabilities for the entire vocabulary. This partial action approach maintains word selection accuracy for the important cases while reducing energy consumption by avoiding unnecessary computations for less relevant words.
Data Source
AI summary
A language model may be used in a variety of natural language processing tasks, such as speech recognition, machine translation, sentence completion, part-of-speech tagging, parsing, handwriting recognition, or information retrieval. A natural language processing task may use a vocabulary of words, and a word hash vector may be created for each word in the vocabulary. A sequence of input words may be received, and a hash vector may be obtained for each word in the sequence. A language model may process the hash vectors for the sequence of input words to generate an output hash vector that describes words that are likely to follow the sequence of input words. One or words may then be selected using the output word hash vector and used for a natural language processing task.