Segmented Text Stream Processing with Coordinated Data Moderation Graph
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Solution Overview
Problem
Conventional large language models (LLMs) face challenges in managing complex interactions, hardware overtaxation, and content moderation, particularly when processing multiple input prompts and integrating evolving models, which complicates user experience and increases the risk of generating undesired content.
Innovation Solution
The implementation of meta-model topologies and model graphs that segment data prompts, prune nodes based on policy information, and generate discrete instances for processing, allowing for efficient content moderation and improved user experience by abstracting away model complexity and enabling scalable, dynamic batching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are interlinked to process different prompts, then the functionality and versatility of the system is improved, but the device complexity and difficulty of coordination increase
Solution Approach 1:
The patent segments the monolithic model processing into discrete model nodes organized in a graph structure, where each node represents a specific machine learning model. This segmentation allows independent management and coordination of individual models while maintaining overall system functionality, directly addressing the complexity issue described in the background.
Solution Approach 2:
The patent introduces an intermediary graph execution engine that mediates between user prompts and multiple machine learning models. This intermediary coordinates model selection, execution order, and result aggregation, simplifying the complexity of directly managing interlinked models while preserving system versatility.
2Productivity
If multiple machine learning models process multiple input prompts simultaneously, then the productivity and output capacity increase, but the hardware becomes overtaxed
Solution Approach 1:
The patent implements dynamic model graph instantiation where the system creates and configures model execution graphs in real-time based on prompt requirements. This dynamic approach allows the system to optimize resource allocation for each specific processing task, preventing hardware overtaxation while maintaining high productivity through selective model execution.
Solution Approach 2:
The patent changes system parameters by dynamically selecting and configuring which models execute based on prompt analysis. This parameter-based model selection optimizes computational resource usage by activating only the necessary models for each task, thereby increasing productivity without consistently overtaxing hardware resources.
3Reliability
If content moderation is applied to filter undesired content, then the reliability and safety of outputs improve, but the processing time and latency increase
Solution Approach 1:
The patent applies content moderation nodes as preliminary filters within the model graph execution pipeline. By integrating moderation checks at strategic points before content generation and at output stages, the system ensures safety without requiring complete reprocessing, thereby maintaining reliability while minimizing latency through targeted rather than comprehensive moderation.
Data Source
AI summary
A model graph receives a data prompt as input. The data prompt is segmented into multiple segments. An instance of the model graph is generated for each segment of the data prompt. Each instance of the model graph is also pruned according to policy information associated with the model graph instance's corresponding data prompt segment. Each instance of the model graph generates an intermediary output. A final output of the model graph for the entire data prompt is generated based on a combination of the intermediary outputs.