LLM 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 outputs due to their stochastic nature, which are not acceptable for industrial applications, necessitating improved guard-railing methods.
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 similarity and adversarial metrics to ensure it stays within the domain of interest, using knowledge graphs (KGs) for verification and uncertainty quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used for content generation, then creativity and contextual relevance are improved, but hallucination and accuracy deteriorate
Solution Approach 1:
The patent introduces an information model as an intermediary between the LLM and the output. The LLM generates content based on natural language input, but the information model acts as a mediator to verify and validate the generated content against domain-specific knowledge, thereby reducing hallucinations while preserving the LLM's contextual relevance capabilities
Solution Approach 2:
The system implements a feedback mechanism where the generated content is compared against the information model, and the result feeds back into the generation process. This allows the system to identify and correct hallucinated content, improving accuracy while maintaining the creative and contextual capabilities of the LLM
2Adaptability or versatility
If LLMs are used for content generation, then coherence and contextual relevance are improved, but determinism and reproducibility deteriorate
Solution Approach 1:
The information model is prepared in advance with structured domain knowledge before the LLM generation process. This preliminary structuring of knowledge provides a deterministic framework that constrains the stochastic LLM output, ensuring that while the LLM can generate contextually coherent content, the final output is reproducible by validating against the pre-established information model
Solution Approach 2:
The information model serves as a deterministic intermediary that translates the stochastic LLM output into reproducible results. By mediating between the non-deterministic LLM and the required deterministic output, it preserves contextual coherence while ensuring reproducibility through consistent validation rules
3Reliability
If guard-railing is applied to LLM output, then hallucination is reduced, but device complexity increases
Solution Approach 1:
The system segments the guard-railing function into a separate, modular information model component. Rather than embedding complex validation logic throughout the LLM system, the information model is a distinct module that contains domain-specific knowledge structures, making the overall system more manageable and easier to maintain while effectively reducing hallucinations
4Measurement precision
If domain-specific information models are used for verification, then output accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial verification by focusing the information model validation on critical domain-specific aspects of the generated content rather than every detail. This selective verification approach maintains high output accuracy for important elements while reducing the overall processing time compared to exhaustive verification of all generated content
Data Source
AI summary
A method for guard-railing output from a generative AI model in an industrial plant context includes 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, and providing a second output based on a result of the comparing.