LLM Output Guard-Railing Using Domain Information Models
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Solution Overview
Problem
Large Language Models (LLMs) face challenges such as potential biases, inaccurate or hallucinated content generation, and non-deterministic output, which are not acceptable for industrial applications, necessitating improved hallucination reduction and guard-railing.
Innovation Solution
A method that compares LLM-generated content to a reference information model (IM) associated with a domain, filtering or modifying the output based on the comparison to ensure it stays within the domain of interest, using a joint embedding space to determine distances and categorize content as permitted, adversarial, or intermediate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM generates content based on stochastic neural network architecture, then creativity and contextual relevance are improved, but hallucination and non-deterministic output increase
Solution Approach 1:
The patent introduces an information model as an intermediary between the LLM and the output. The LLM generates content based on user input, but this content must then be verified against the structured information model before being presented as final output. This mediator ensures that while the LLM maintains its creative and contextual capabilities, the final output is grounded in factual accuracy and domain-specific knowledge.
Solution Approach 2:
The system implements a feedback mechanism where the generated content is continuously evaluated against the information model. The verification process provides feedback to determine whether content should be accepted, modified, or rejected. This feedback loop ensures that hallucinations are detected and corrected while preserving the LLM's ability to generate contextually relevant responses.
2Reliability
If LLM output is strictly verified against information model, then hallucination is reduced, but output flexibility and creativity are limited
Solution Approach 1:
The verification process applies different levels of scrutiny to different parts of the generated content. Critical factual claims are strictly verified against the information model, while creative or interpretive elements allow more flexibility. This local quality approach ensures that reliability is maintained where it matters most while preserving creativity and flexibility in appropriate areas.
Solution Approach 2:
The system applies verification selectively rather than uniformly to all content. Not every generated token undergoes the same level of strict verification - instead, the system focuses verification efforts on key factual assertions and domain-specific claims, allowing partial verification that maintains both reliability and flexibility.
3Reliability
If domain knowledge is embedded in structured information model, then guard-railing capability is improved, but system complexity increases
Solution Approach 1:
The system segments domain knowledge into structured information models that are separate from the LLM itself. This segmentation allows the knowledge base to be independently developed, maintained, and updated without affecting the LLM architecture. The information model acts as a modular component that can be verified against independently, reducing overall system complexity.
Solution Approach 2:
The patent extracts domain knowledge from the LLM's internal representations and places it into external structured information models. This extraction separates the factual knowledge base from the generative capabilities, allowing each to be optimized independently. The information model contains only the essential domain knowledge needed for verification, reducing complexity while maintaining guard-railing effectiveness.
Data Source
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AI summary
There is disclosed a method for guard-railing output from a generative AI model in an industrial plant context. The method comprises comparing at least part of content of a first output provided by the generative AI model to content of a reference information model associated with a reference domain. The method further comprises providing a second output based on a result of the comparing.