Variational Generative Models With Intermediate Decision Pruning
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Solution Overview
Problem
Variational generative autoencoders lack flexibility to incorporate intermediate control factors, limiting their ability to capture complex dependencies and thus their applicability in real-world scenarios, and large language models face significant challenges due to extensive size and computational costs, particularly in resource-constrained environments.
Innovation Solution
A method and system for modifying machine-learning models by determining an intermediate decision parameter based on a latent space, dynamically pruning the model structure using rules conditioned on this parameter, and generating outputs conditioned on both the latent variable and the intermediate decision parameter, thereby enhancing flexibility and reducing model size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If variational generative autoencoders are used to generate outputs based on latent variables and input conditions, then the model can capture complex data distributions, but the model lacks flexibility to incorporate intermediate control factors
Solution Approach 1:
The patent introduces an intermediate decision parameter as a mediator between the latent variable and the final output. This intermediate parameter enables the model to incorporate additional control factors without fundamentally redesigning the entire architecture. The intermediate decision parameter acts as a bridge that allows flexible control while maintaining the existing variational generative framework, thus resolving the contradiction between adaptability and complexity.
2Reliability
If large language models are used for natural language processing tasks, then model performance is improved, but computational costs and model size increase significantly
Solution Approach 1:
The patent segments the large language model into multiple smaller specialized models, each trained to handle specific tasks or aspects of language processing. This segmentation allows the system to achieve high performance on specific tasks using smaller, more computationally efficient models rather than deploying a single large model for all tasks, thus reducing overall computational costs while maintaining reliability.
Solution Approach 2:
The patent implements dynamic model selection and结构调整 where the system can adaptively choose between different model sizes and configurations based on the specific task requirements and available computational resources. This dynamic approach allows the system to use smaller models when sufficient and larger models only when necessary, optimizing the balance between performance and computational cost.
3Adaptability or versatility
If large language models are deployed in resource-constrained environments, then comprehensive AI capabilities are provided, but the extensive model size and computational requirements pose significant challenges
Solution Approach 1:
The patent creates a universal framework that can operate with multiple model configurations tailored to different resource constraints. The system provides comprehensive AI capabilities across diverse environments by allowing the same architectural framework to accommodate everything from small edge devices to large servers, selecting appropriate model sizes and types for each deployment scenario.
Data Source
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AI summary
Provided is a method for modifying a machine-learning model. The method includes performing, by a machine learning model (122), a generative process to predict a first output (125), generating, via a processor (110), a latent space based on an input (121) to the machine learning model (122), determining, via the processor (110), an intermediate decision parameter (124) based on the latent space, based on the intermediate decision parameter (124), changing, via the processor (110), a structure of the machine learning model (122) to generate a modified machine learning model (126) to perform a modified generative process that is conditioned upon the intermediate decision parameter (124), and generating, by the modified machine learning model (126), a second output (125) including content associated with the input (121).