AI Completeness Graph Generation with Reinforcement Learning
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Solution Overview
Problem
Conventional methods for generating and updating completeness graphs in compliance regimes are resource-intensive, time-consuming, and prone to errors due to the manual labor involved, especially when new rules and regulations are added or modified.
Innovation Solution
A transformer-based reinforcement learning approach is used to train a large language model to automatically generate and optimize completeness graphs, leveraging domain-specific knowledge repositories and reward models to enhance accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to author completeness graphs, then accuracy and compliance with regulations are maintained, but resource expenditure and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical authoring of completeness graphs with an automated AI-based system. A language model generates completeness graphs from regulatory text, and a reinforcement learning agent iteratively refines them based on feedback from a compliance engine, eliminating the need for manual graph construction while maintaining accuracy.
Solution Approach 2:
The system enables self-service through automated generation and validation. The completeness graph generator autonomously processes regulatory documents, generates initial graphs, and iteratively refines them using reinforcement learning feedback without requiring manual intervention, allowing the system to serve itself in the graph generation process.
2Reliability
If manual methods are used to update completeness graphs for new regulations, then compliance accuracy is maintained, but resource expenditure and complexity increase
Solution Approach 1:
The patent replaces manual update processes with an automated AI system that processes new regulatory documents, generates updated completeness graphs, and validates them through reinforcement learning. This substitution eliminates manual intervention in the update process while maintaining compliance accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where the reinforcement learning agent receives feedback from the compliance engine about generated completeness graphs. This feedback loop enables continuous improvement and validation of graph accuracy against actual compliance requirements, ensuring reliability without increasing operational complexity.
3Productivity
If automated AI methods are used to generate completeness graphs, then resource expenditure and time consumption are reduced, but generation accuracy and compliance may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the reinforcement learning agent receives feedback from a compliance engine about the generated completeness graphs. The agent uses this feedback to iteratively refine and improve the accuracy of generated graphs, ensuring high reliability while maintaining automated high-speed generation.
Solution Approach 2:
The system performs preliminary generation of completeness graphs using language models, then subjects them to iterative refinement through reinforcement learning before final validation. This preliminary action allows for rapid initial generation followed by targeted improvements, achieving both speed and accuracy.
Data Source
AI summary
Systems and methods are described for training a large language model to operate as a completeness graph generator to automatically generate completeness graphs in response to queries based on instructions including forms, rules, and regulations. A dataset is obtained that includes instructions and associated ground truth completeness graphs, previously generated manually by domain experts. An active large language model is trained configured to produce a generated completeness graph in response to a query that is evaluated with a reward model based on validity of the generated completeness graph and semantic similarity of the generated completeness graph and the associated ground truth completeness graph. The active large language model is re-trained based at least partially on the reward.