Gap Detection Workflow for LLM Knowledge Error Remediation
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Solution Overview
Problem
Existing artificial intelligence systems, particularly Large Language Models (LLMs), suffer from issues such as the need for frequent retraining, hallucinations, and unpredictable errors due to their black box nature, leading to skewed data and inefficient learning.
Innovation Solution
A method involving a processor that identifies possible causes for errors or gaps by testing against a set of potential causes and eliminating those that do not result in errors, allowing for targeted remediation of specific knowledge or communication gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are trained frequently to stay aware of new events, then their knowledge is updated, but their system complexity and computational requirements increase
Solution Approach 1:
The patent segments the knowledge base into static foundational knowledge and dynamic gap-filled knowledge. The LLM maintains a stable core model while using a separate gap detection and filling mechanism to handle updates, avoiding the need to retrain the entire system frequently.
Solution Approach 2:
The patent introduces an intermediary gap detection system that acts as a mediator between the LLM and new information. This intermediary component identifies knowledge gaps and fills them without requiring changes to the core LLM model, thus maintaining system stability while enabling knowledge updates.
2Adaptability or versatility
If LLMs are retrained each time technology is updated, then they reflect new data, but loss of time and computational resources increase
Solution Approach 1:
The patent performs preliminary gap detection before full retraining is needed. By identifying and filling knowledge gaps in advance using the gap detection system, the system prepares for future updates without requiring immediate complete retraining, thus saving time and computational resources.
Solution Approach 2:
The patent discards the need for complete model retraining by separating gap filling from the core model. The gap detection system identifies what needs to be updated, and only those specific gaps are filled, rather than discarding and recovering the entire model, thus reducing time and computational overhead.
3Speed
If LLMs operate as black box systems, then they process information quickly, but measurement precision of their internal operations is unknown
Solution Approach 1:
The patent implements feedback mechanisms through gap detection that monitor LLM outputs and identify knowledge gaps. This feedback loop provides measurement precision by detecting when the system is uncertain or hallucinating, while maintaining the speed of black box processing for standard operations.
Solution Approach 2:
The patent replaces the need for complete transparency into LLM internal mechanics with a gap detection system that operates at the output level. Instead of trying to measure internal operations, the system substitutes by detecting the effects of those operations (knowledge gaps) and addressing them accordingly.
4Reliability
If human tuning is applied to fix LLM hallucinations, then accuracy improves, but device complexity and unpredictability increase
Solution Approach 1:
The patent implements self-service by enabling the system to detect and fix its own hallucinations through automated gap detection. Instead of requiring continuous human tuning, the system autonomously identifies knowledge gaps and fills them, reducing the need for human intervention while maintaining reliability.
Solution Approach 2:
The patent changes the approach from manual parameter tuning to automated gap detection and filling. By transforming the tuning process into a systematic detection and filling mechanism, the system achieves reliability improvements without the complexity and unpredictability of human tuning operations.
Data Source
AI summary
A method of determining and repairing gaps is presented involving noting errors and providing an indication of a set of possible causes of the errors. The set is then tested individually to identify which causes remain and which can be filtered. Remediation of those causes that remain is then optionally undertaken.