Custom Vector Embeddings for Domain-Specific Query Understanding
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Solution Overview
Problem
Large language models struggle to interpret and apply domain-specific language effectively, necessitating a method to train custom vector embedding models that can represent and process such language.
Innovation Solution
A method involving a large language model to generate synthetic variant questions, create question pairs, and train a custom vector embedding model to maximize differences between dissimilar pairs and minimize similarities between similar pairs, thereby reprojecting vector embeddings into a custom space suitable for domain-specific language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a default vector embedding model is used, then the system can process general language tasks, but it struggles to interpret and apply domain-specific language effectively
Solution Approach 1:
The patent transforms the embedding space by learning a domain-specific projection matrix that maps general language embeddings to domain-adapted embeddings. This parameter transformation allows the system to maintain the benefits of general language models while adapting to domain-specific terminology and concepts through learned parameter adjustments in the embedding space.
Solution Approach 2:
The patent introduces an intermediate domain adaptation layer that acts as a mediator between the default embedding model and the domain-specific processing requirements. This intermediary component transforms general embeddings into domain-specific representations without requiring direct modification of the original embedding model, enabling flexible domain adaptation.
2Adaptability or versatility
If a custom vector embedding model is trained from scratch, then it can be optimized for domain-specific language, but it requires extensive training data and computational resources
Solution Approach 1:
The patent performs preliminary action by pre-training on a large corpus to establish a robust default embedding space, then subsequently adapts this pre-trained model to domain-specific language through targeted domain adaptation training. This preliminary preparation reduces the burden of training from scratch while maintaining domain-specific optimization capabilities.
Solution Approach 2:
The patent creates a copied and adapted version of the default embedding model specifically for domain-specific tasks. Instead of modifying the original model directly, it generates a domain-adapted copy that inherits the general language capabilities while adding domain-specific specialization through learned transformation parameters.
3Quantity of substance
If synthetic variant questions are generated using a large language model, then the training data can be expanded, but it increases computational processing time
Solution Approach 1:
The patent implements self-service by using the large language model to automatically generate synthetic training data without requiring manual creation. The system serves its own training data needs by leveraging the LLM's capability to generate diverse question variants, eliminating the need for external data collection efforts while expanding the training corpus.
Solution Approach 2:
The patent applies universality by using the large language model for multiple functions: both generating synthetic training data and potentially serving as the base model for domain adaptation. This multi-functional approach maximizes the utility of the LLM while reducing overall system complexity and resource requirements.
Data Source
AI summary
Systems and methods for training and using custom vector embedding models are provided. The systems and methods involve operating at least one processor to: receive a plurality of example user questions containing domain-specific language; use a large language model to create a plurality of synthetic variant questions based on the plurality of the example user questions; generate a plurality of similar question pairs; use the large language model to assign a plurality of topics to the plurality of example user questions and the plurality of synthetic variant questions; generate a plurality of dissimilar question pairs; determine, using a default vector embedding model, default vector embeddings for each similar question pair and each dissimilar question pair; and train the custom vector embedding model based on differences between the default vector embeddings of each similar question pair and each dissimilar question pair.