Knowledge Graph Media Retrieval 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 reduced understandability and accuracy, especially when generating lengthy or complex text.
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 in real-time responses, enhancing comprehension and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large language models generate purely textual responses, then the models can maintain operational simplicity, but the responses lack integration of non-textual content reducing understandability and accuracy
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary component that bridges the gap between the large language model and non-textual media content. The knowledge graph processes user prompts, identifies relevant media files, and returns media identifiers to the model, enabling the integration of images, audio, and video without directly modifying the language model's core architecture.
Solution Approach 2:
The system is divided into distinct functional modules: the large language model for text processing, the knowledge graph for semantic understanding and media identification, and separate media rendering components. This segmentation allows each component to specialize in its strength while working together to deliver multi-modal responses.
2Adaptability or versatility
If large language models process only textual input, then the processing speed remains fast, but the models cannot effectively integrate visual and audio content
Solution Approach 1:
The knowledge graph performs preliminary processing of user prompts before they reach the large language model. By pre-identifying relevant media content and preparing media identifiers in advance, the system reduces the processing burden on the model during the actual response generation phase, maintaining speed while enabling multi-modal integration.
3Ease of operation
If the system integrates multiple media types in responses, then user interaction quality improves, but the system complexity increases
Solution Approach 1:
The knowledge graph serves as a universal intermediary that handles multiple types of media content (images, audio, video) through a single unified interface. Rather than creating separate processing pipelines for each media type, the system uses one multi-functional component to identify and retrieve diverse media formats, simplifying the overall architecture while maintaining versatility.
Data Source
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.