Hierarchical LLM Sequencing for Lower-Load AI Supercomputing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AI generative models and large language models (LLMs) require significant supercomputing efforts to improve response times, accuracies, and reduce computational load, limiting their scalability and expandability.
Innovation Solution
A multi-level generative AI system utilizing derived requests, multiple h-LLMs with varying levels of accuracy, and a distributed architecture to efficiently process and combine results, incorporating techniques like bagging, boosting, and merging/fusing h-LLMs to enhance precision and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning algorithms are used for content analysis, then computational efficiency is maintained, but the ability to generate human-like content and capture complex language patterns is limited
Solution Approach 1:
The system segments the monolithic LLM into multiple smaller specialized models (h-LLMs) that each handle specific tasks or domains. This segmentation allows parallel processing of different aspects of language generation, improving overall efficiency while maintaining high accuracy through specialized architectures optimized for their respective functions.
Solution Approach 2:
The patent implements a nested architecture where multiple hierarchical levels of LLMs are organized in layers. Higher-level models coordinate and synthesize outputs from lower-level specialized models, creating a nested structure that enables complex language tasks to be broken down into manageable sub-tasks, reducing computational overhead while preserving linguistic nuance.
2Adaptability or versatility
If a single large LLM is used to handle all generative tasks, then model versatility is achieved, but system complexity and computational load increase
Solution Approach 1:
The patent creates a universal system architecture that can handle diverse generative tasks through a standardized interface and coordination layer. Multiple specialized h-LLMs are designed to work together within this universal framework, allowing the system to adapt to various tasks (text generation, code generation, multilingual translation) without requiring separate monolithic models for each function.
Solution Approach 2:
The system dynamically routes queries to appropriate specialized models based on task requirements, language type, and complexity. This dynamic allocation allows the system to maintain versatility by adapting its composition of models in real-time, while keeping individual model complexities manageable through automated routing and load balancing mechanisms.
3Measurement precision
If LLM training data is expanded to improve accuracy and reduce bias, then model precision improves, but computational requirements and training time increase
Solution Approach 1:
Training data is segmented and distributed across multiple specialized h-LLMs according to their specific domains and functions. Each model is trained on curated, domain-specific datasets rather than requiring all models to process the entire training corpus, significantly reducing training time while improving accuracy through focused, high-quality data for each specialization.
Solution Approach 2:
The system performs preliminary processing and curation of training data before distribution to specialized models. Data is pre-filtered, annotated, and organized into domain-specific datasets in advance, enabling faster training cycles for each h-LLM while ensuring high data quality that improves model accuracy without requiring retraining on raw unprocessed data.
4Speed
If LLM response time is reduced through optimized processing, then user experience improves, but model accuracy and thoroughness may deteriorate
Solution Approach 1:
The generation process is segmented into parallel processing streams where different aspects of content creation (structure, language, domain knowledge) are handled simultaneously by specialized models. This parallel segmentation enables faster overall response times while maintaining accuracy through coordinated synthesis of multiple specialized outputs rather than sequential processing of a single model.
Solution Approach 2:
The system performs preliminary structuring and planning of generated content before final language generation. Specialized models pre-process information, organize content structure, and prepare domain-specific knowledge in advance, allowing the final generation phase to proceed quickly while maintaining accuracy through pre-validated content frameworks and structured approaches.
Data Source
AI summary
A system and method for creating a merged large language model (h-LLM) using a bagging approach including receiving input data at a computer system, creating a plurality of data subsets from the input data, training a plurality of h-LLMs, each h-LLM of the plurality of h-LLMs being trained on a respective data subset of the plurality of data subsets, creating a merged h-LLM by merging the plurality of h-LLMs, and outputting the merged h-LLM.