Knowledge Graph Media Linking for Multimodal LLM Responses
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Solution Overview
Problem
Large language models struggle to process and integrate non-textual content, such as images and audio, in their responses, leading to less understandable and less accurate interactions with users.
Innovation Solution
Utilizing a semantic data store like a knowledge graph to associate media files with user prompts, enabling large language models to access and integrate media content, such as images, video, or audio, by linking relevant nodes and providing media file identifiers or content in responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models process only textual content, then the model complexity remains manageable, but the response comprehensibility and accuracy deteriorate when non-textual content is needed
Solution Approach 1:
The system segments the content processing task by separating textual content handling (performed by the large language model) from non-textual content handling (performed by the semantic data store). This allows each component to specialize in its strengths without increasing overall system complexity, thereby improving response accuracy for multi-modal queries.
Solution Approach 2:
The patent introduces a semantic data store as an intermediary between the user prompt and the large language model. This mediator translates non-textual content into semantic representations that the LLM can process, enabling accurate handling of images, audio, and video without directly increasing model complexity.
2Measurement precision
If large language models generate only text responses, then the ease of operation is maintained, but the response comprehensibility deteriorates when visual or audio content is required
Solution Approach 1:
The system achieves multi-functionality by enabling the large language model to handle multiple content types (text, images, audio, video) through the semantic data store intermediary. This universal interface maintains ease of operation for users while dramatically improving response comprehensibility by providing appropriate media types for different query contexts.
Solution Approach 2:
The system changes the parameter of response format from purely textual to multi-modal (text, image, audio, video) based on the semantic analysis of the user prompt. This dynamic parameter adjustment improves comprehensibility while maintaining a simple, consistent user interface that requires no special operation knowledge.
3Measurement precision
If the semantic framework includes at least one thousand elements, then the response accuracy improves through comprehensive analysis, but the processing time increases
Solution Approach 1:
The semantic data store pre-processes and structures non-textual content into semantic representations before they reach the large language model. This preliminary action organizes the one thousand+ semantic elements in advance, enabling comprehensive analysis without adding significant processing time during the actual query response generation.
Data Source
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AI summary
The present disclosure provides techniques enabling large language models (LLMs) to access and integrate media content, such as images, video, or audio, using a semantic data store like a knowledge graph. The disclosed techniques involve processing user prompts through a knowledge graph to identify relevant nodes linked to media files. These media files or their identifiers are then provided to the LLM, enhancing response accuracy and comprehensibility. The techniques also include creating new classes in the knowledge graph to represent media files with properties like type, location, and associations. This approach allows LLMs to deliver integrated textual and visual content in real-time, improving user interaction and response quality. Furthermore, the techniques allow the general knowledge of an LLM to be supplemented with media files, and optionally other information, in the knowledge graph. The techniques are fundamentally computer-implemented, leveraging technologies such as RDF triples, named entity recognition, and vector embeddings.