Automatic Multimodal Graph Construction for Audio-Text Modeling
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Solution Overview
Problem
Existing knowledge graphs (KGs) rely heavily on human annotations, which are laborious and expensive, limiting scalability, and generative large language models (LLMs) suffer from hallucinations and lack controllable multimodal integration with audio signals.
Innovation Solution
Utilize predefined semantic frames and LLMs to automatically construct multimodal knowledge graphs from audio datasets, mitigating hallucinations by validating edges and nodes, enabling scalable and controlled audio-text modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotations are used to construct knowledge graphs, then the quality and accuracy of the knowledge graph is improved, but the labor cost and time consumption increase significantly
Solution Approach 1:
The patent uses LLMs to generate synthetic knowledge graph data that copies and replicates the structure and content of manually annotated knowledge graphs. The LLM generates triplets (subject, predicate, object) that mimic human-annotated data, enabling automated knowledge graph construction without requiring actual human annotation for each dataset, thus resolving the contradiction between accuracy and time consumption
Solution Approach 2:
The system enables knowledge graphs to construct themselves automatically using LLMs that process audio and text data autonomously. The LLM performs self-annotation by extracting entities, relationships, and attributes directly from the data without human intervention, allowing the knowledge graph to serve its own construction needs and eliminating the time-consuming human annotation process while maintaining quality through the LLM's sophisticated understanding
2Productivity
If generative LLMs are used for knowledge graph construction, then productivity is improved, but hallucination effects reduce reliability
Solution Approach 1:
The patent implements a feedback mechanism where the LLM-generated knowledge graph triplets are validated against the original audio and text data. The system checks whether the generated entities, predicates, and objects accurately reflect the source material, providing feedback to correct or reject hallucinated content. This feedback loop maintains reliability while preserving the high productivity of automated LLM generation
Solution Approach 2:
The system applies preliminary anti-action by pre-validating LLM-generated knowledge graph content against ground truth data before final incorporation. The validation process proactively identifies and mitigates hallucinations before they can propagate through the knowledge graph, preventing reliability issues rather than correcting them afterward, thus maintaining both productivity and reliability
3Loss of information
If LLMs infer supplemental data for graph nodes, then completeness of the knowledge graph is improved, but the risk of hallucination increases
Solution Approach 1:
The patent applies partial action by having the LLM infer only the specific supplemental data that is missing from the knowledge graph nodes, rather than generating all possible information. The system selectively infers only the necessary attributes, relationships, or entities that would complete the knowledge graph structure, minimizing the risk of hallucination while improving completeness. This targeted approach balances information completeness with data accuracy by limiting inference to only what is essential
Data Source
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AI summary
Knowledge-based audio-text modeling via automatic multimodal graph construction is performed. An audio dataset is received, the audio dataset including clips of audio data, wherein each of the clips of the audio data is paired with corresponding metadata descriptive of the audio contents of the respective clip of the audio data. Graph nodes of interest are identified from a sematic network, the graph nodes being descriptive of semantics of the knowledge domain of the contents of the audio dataset. A large language model (LLM) is utilized for categorizing the metadata into the graph nodes and for inferring supplemental data for the graph nodes for which there is no metadata, producing an extracted knowledge graph. The extracted knowledge graph is validated utilizing the LLM to perform relation verification of edges between the graph nodes of the extracted knowledge graph, thereby mitigating hallucination effects in the categorizing and inferring of the supplemental data.