Content Management Large Language Model Recommendations via Knowledge Graphs
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Solution Overview
Problem
Existing digital content systems are operationally inflexible and inefficient in selecting large language models, leading to improper and inaccurate model selections due to a conventional context-free interaction paradigm, excessive navigation, and inefficient use of computational resources.
Innovation Solution
A model modification system that utilizes a knowledge graph to determine relationships between user accounts, content items, and large language models or virtual assistants, providing personalized recommendations based on contextual data within a content management system, reducing navigational interactions and preserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional context-free interaction paradigm is used to provide a generic repository of all available large language models, then users can access any model in the repository, but the system suffers from operational inflexibility and improper, uninformed, and inaccurate model selections
Solution Approach 1:
The system performs preliminary actions by proactively analyzing user context, content items, and usage patterns before model selection is needed. It pre-determines suitable large language models and presents them to users, eliminating the need for users to manually search through repositories and making informed selections automatically.
Solution Approach 2:
The system introduces an intermediary recommendation engine that mediates between the user and the large language model repository. This intermediary analyzes contextual factors and selectively presents appropriate models, bridging the gap between user needs and model availability while improving selection accuracy.
2Productivity
If existing systems require excessive numbers of client device interactions for identifying and selecting large language models, then users can access available models, but the system consumes excessive computational resources such as processing power and memory
Solution Approach 1:
The system performs preliminary analysis of user context, content items, and usage patterns to pre-determine suitable large language models before any user interaction occurs. This eliminates the need for users to navigate through multiple interfaces and reduces computational resources spent on processing excessive navigation interactions.
Solution Approach 2:
The system merges the model recommendation functionality with the existing content management interface, consolidating what were previously separate interfaces into a unified system. This reduces navigational overhead and computational resources required for maintaining and processing data across multiple separate applications.
3Ease of operation
If interfaces for accessing content items are entirely separate from interfaces for selecting and interacting with large language models, then users can access content and models independently, but the system suffers from navigationally inefficient access and wasted computer memory for caching data
Solution Approach 1:
The system merges the content management interface and large language model selection interface into a unified system. Users can access both content items and model recommendations within a single interface, eliminating the need to navigate between separate applications and reducing memory requirements for caching data across multiple interfaces.
Solution Approach 2:
The unified interface serves multiple functions: it displays content items, provides contextualized large language model recommendations, and enables model selection and interaction. This multi-functional approach eliminates the need for separate specialized interfaces, improving navigational efficiency and reducing memory usage.
Data Source
AI summary
This disclosure describes systems that identify one or more models (e.g., large language models and/or virtual assistants) permitted to access content items stored for user accounts within a content management system. The disclosed systems can determine a model available to a user account within the content management system from among the one or more models. For example, the disclosed systems can determine one or more relationships between the user accounts within the content management system, large language models utilized by the user accounts, virtual assistants utilized by the user accounts, and content items accessed by the user accounts. The disclosed systems can determine the model for the user account according to the one or more relationships. The disclosed systems can provide a notification corresponding to the model via a user interface of a client device associated with the user account.