Meta-Model Topology Integration for Content Moderation Graphs
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Solution Overview
Problem
Conventional machine learning models, particularly large language models (LLMs), face challenges in managing complex interactions, hardware overtaxing, and generating undesired content, such as hate speech, due to the complexity of integrating multiple models and evolving frameworks.
Innovation Solution
The deployment and moderation of meta-model topologies and model graphs that incorporate a global label schema, allowing for streamlined integration and moderation of content across multiple models, including functions for content moderation, annotation, and generation, with dynamic batching and auto-scaling to improve efficiency and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are integrated to process input prompts, then the functionality and versatility of the system is improved, but the device complexity and difficulty of operation increase
Solution Approach 1:
The patent combines multiple discrete machine learning models into a unified model graph structure where nodes represent models and edges represent data flow. This merging approach maintains the functionality of individual models while presenting a simplified unified interface to users, reducing operational complexity despite increased versatility.
Solution Approach 2:
The model graph framework serves as a universal container that can accommodate various types of machine learning models (LLMs, classifiers, generators) with different functions. This multi-functional framework allows diverse models to be integrated through a common interface, improving versatility without proportionally increasing operational difficulty.
2Adaptability or versatility
If multiple machine learning models are used to process prompts, then the system capabilities are improved, but the ease of operation deteriorates as users must navigate through different models and interfaces
Solution Approach 1:
The model graph acts as an intermediary layer between users and the complex ensemble of machine learning models. Users interact with the unified model graph interface rather than navigating individual model interfaces, which maintains access to multiple model capabilities while significantly improving ease of operation.
3Adaptability or versatility
If computer hardware processes workloads from multiple users and models, then the service coverage is improved, but the reliability deteriorates as hardware becomes overtaxed
Solution Approach 1:
The system dynamically scales and adjusts model graph instances based on workload demands. When hardware becomes overtaxed from serving multiple users, the system can dynamically instantiate additional model graph copies or adjust resource allocation, maintaining service coverage while preventing hardware overload and preserving reliability.
4Adaptability or versatility
If new machine learning models are developed and interlinked with existing models, then the adaptability is improved, but the device complexity increases making coordination and integration difficult
Solution Approach 1:
The patent segments the integration process into standardized components: models are represented as discrete nodes with defined input/output interfaces, and relationships are represented as edges. This segmentation allows new models to be independently developed and then easily integrated into the model graph by connecting nodes through standardized interfaces, improving adaptability while managing integration complexity.
Data Source
AI summary
A meta-model topology comprises a plurality of functions and conforms to a global label schema. A new function not included in the plurality of functions is integrated into the meta-model topology. A particular label of interest that is associated with the new function is identified and the new function is configured such that an output from the new function conforms to an output form corresponding to the particular label of interest from the global label schema. The new function is then integrated into the meta-model topology and the meta-model topology that includes the new function is used to generate a model graph. The model graph is then deployed to a remote application that is configured to receive data prompts comprising input data processed by nodes of the model graph.