Generative AI Content Integration Without LLM Retraining
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Solution Overview
Problem
Training neural networks to reduce hallucinations requires significant computational resources and is often ineffective, leading to unusable models due to the difficulty in identifying and correcting erroneous data.
Innovation Solution
Utilizing an untrained large language model (LLM) with encoded domain knowledge from a content repository, where queries are vectorized and combined with context vectors to generate responses without requiring domain-specific training, and errors are addressed by excluding error-producing content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural network is trained to reduce hallucinations, then the accuracy of responses is improved, but the computational resources and processing time required increase significantly
Solution Approach 1:
The system segments the knowledge base into individual documents or content units, each processed independently to generate context vectors. This allows selective processing of only relevant documents for each query, rather than training the entire model on all knowledge, significantly reducing computational resources while maintaining accuracy.
Solution Approach 2:
The patent introduces context vectors as an intermediary between the untrained neural network and the knowledge base. These vectors encode domain knowledge without requiring model retraining, allowing the system to achieve accurate responses while avoiding the high computational cost of training.
2Reliability
If a neural network is retrained to add new content or correct errors, then the reliability is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary processing of the knowledge base by generating context vectors from documents in advance. When new content is added or errors need correction, only the affected context vectors need to be updated rather than retraining the entire model, significantly reducing the time and resources required.
Solution Approach 2:
The patent changes the approach from modifying model parameters through training to modifying context vector parameters. By updating context vectors directly rather than retraining the neural network, the system can quickly incorporate new content or correct errors with minimal time and computational resources.
3Measurement precision
If domain-specific training is applied to an untrained neural network, then the domain knowledge accuracy is improved, but the computational resources and complexity increase
Solution Approach 1:
The patent introduces context vectors as an intermediary that carries domain knowledge to the untrained neural network. This approach achieves domain-specific accuracy without the complexity of training the model, as the knowledge is embedded in the context vectors rather than the model parameters.
Solution Approach 2:
The system replaces the mechanical training process with a vector-based knowledge encoding approach. Instead of using expensive GPU training infrastructure and complex optimization algorithms, the system uses simpler vector operations to achieve the same goal of domain knowledge integration.
4Reliability
If the training set is purged and retrained to correct prevalent hallucinations, then the reliability is improved, but the loss of time and computational resources increases
Solution Approach 1:
The patent extracts only the problematic or erroneous content from the knowledge base and updates or removes only the corresponding context vectors. This selective approach avoids the need to purge and retrain the entire training set, significantly reducing the time and resources required while maintaining reliability.
Data Source
AI summary
Systems and methods are provided for a device to obtain a query, such as from a user. The query is vectorized to obtain a numerical representation of the query and provided to a vector database to find the nearest vectors corresponding to most relevant context, such as for a particular domain or subject matter. The query, query vector, and context vectors, and optionally past query history and past query responses, are provided to an artificial intelligence, such as a large language model (LLM), to receive a response to the query without providing the context to the LLM.