AI Domain Validation Using Embeddings for Real-Time Compliance
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Solution Overview
Problem
Traditional output validation processes in institutions are labor-intensive, prone to errors, and difficult to navigate due to the complexity of documentation like MDDs and MVDs, which are crucial for ensuring compliance with institution-specific rules.
Innovation Solution
A validation system using large language models (LLMs) for automatic validation operations, trained through transfer learning and domain-specific data sets, allows for real-time compliance checks and query responses, including intelligent storage and management of historical documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of output is performed by managers, supervisors, or quality control agents, then validation can be conducted, but the process becomes labor-intensive and resource-intensive
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated validation system that uses natural language processing, machine learning models, and rule-based engines to perform compliance validation automatically, eliminating the need for manual human review while maintaining or improving accuracy
Solution Approach 2:
The validation system enables self-service by automatically validating outputs against institutional rules and guidelines without requiring human intervention, allowing the system to serve itself in performing compliance checks
2Reliability
If manual review of output is performed, then validation can be conducted, but the process is prone to errors due to varying levels of scrutiny by different reviewers
Solution Approach 1:
The system transforms the validation process from subjective human judgment to objective computational evaluation by changing the parameters of validation from human discretion to standardized algorithmic rules, ensuring consistent application of compliance criteria
Solution Approach 2:
The validation system provides universal application of compliance rules across all outputs and reviewers, ensuring that the same institutional guidelines are applied consistently regardless of who or what performs the validation
3Loss of information
If documentation such as MDDs and MVDs is created manually, then model development and validation can be documented, but the likelihood of errors increases and the documents become dense and complex
Solution Approach 1:
The system performs preliminary action by automatically generating MDDs and MVDs during the model development process itself, rather than creating them separately afterward, ensuring traceability is maintained from the outset while reducing manual effort and errors
Solution Approach 2:
The validation system creates and maintains copies of relevant information across multiple documents automatically, ensuring consistency and traceability without requiring manual replication, thereby reducing errors and complexity
4Loss of information
If historical documents are stored in full form, then complete information is preserved, but storage requirements increase
Solution Approach 1:
The system extracts only the essential contextual information from historical documents that is necessary for validation purposes, storing these extracted elements rather than complete documents, thereby preserving necessary context while reducing storage requirements
Solution Approach 2:
The validation system segments historical documents into discrete, manageable units of contextual information that can be stored and retrieved independently, reducing overall storage requirements while maintaining access to necessary context
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for efficiently handling queries. An example method includes receiving a query from a user device and generating an embedding representation of the query. The example method further includes performing a similarity comparison between the embedding representation of the query and a set of embedding representations of historical document sections stored in a historical document repository and selecting a relevant embedding representation of a historical document section stored in the historical document repository for the query. The example method further includes querying a target large language model using the embedding representation of the query and the relevant embedding representation of the historical document section and providing a query response to the user device.