LLM Quality Monitoring via Expert System Segmentation
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Solution Overview
Problem
Large language models in generative AI systems face challenges in maintaining quality across a vast landscape of topics, as they often experience hallucinations and unintended consequences when trained to improve accuracy in one area, leading to degradation in another.
Innovation Solution
Implementing expert systems, such as knowledge engines, math engines, and translation engines, to evaluate and monitor the quality of responses generated by large language models, using prompt and response pairs to identify areas of improvement and prevent degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are trained to improve accuracy in one area, then accuracy in that specific area is improved, but quality in other areas degrades
Solution Approach 1:
The patent divides the evaluation of model quality into multiple specialized expert systems, each responsible for a specific domain (e.g., math engine for mathematical problems, translation engine for language translation, knowledge engine for factual accuracy). This segmentation allows the system to evaluate different aspects of model performance independently, preventing degradation in one area from affecting the overall assessment of quality in other areas.
Solution Approach 2:
The patent changes the evaluation parameters by using domain-specific metrics for each expert system rather than a single general-purpose metric. Each expert system evaluates responses using parameters optimized for its specific domain, enabling the system to track and maintain quality across multiple dimensions simultaneously, thus preventing the trade-off where improving one parameter degrades another.
2Reliability
If expert systems are used to evaluate model responses, then model quality monitoring is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal evaluation framework that can handle multiple domains through a common architecture. The system uses a standardized interface and process flow that works across different expert systems (math, translation, knowledge), allowing the same core evaluation mechanism to serve multiple functions. This universality reduces the overall complexity compared to implementing separate evaluation systems for each domain.
Solution Approach 2:
The patent introduces an intermediary layer that coordinates between the large language model and multiple expert systems. This mediator manages the evaluation process, routes queries to appropriate expert systems, and aggregates results, thereby simplifying the interaction complexity between components and providing a clean interface for quality monitoring.
Data Source
AI summary
A system is disclosed that uses expert systems to monitor and evaluate quality in a large language model. The system can include a prompt library that associates prompts with areas of expertise. The system selects prompts from the library and evaluates first responses generated by a large language model for the set of prompts against second responses generated by a modified version of the large language model to the set of prompts. The evaluation uses expert systems associated with the areas of expertise for the set of prompts. If the system determines that the evaluation indicates a degradation criterion is met, the system may take remedial action. The system provides an effective way to evaluate and prevent the use of modified language models that do not meet the required standards.