Multi-Tenant Language Model Training With Query Verification
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Solution Overview
Problem
Large language models (LLMs) generate false or hallucinated answers due to the lack of context, undermining user confidence and leading to potential misinterpretation and resource wastage, especially in critical applications like cybersecurity.
Innovation Solution
A system that maps data fields from multiple sources to a predefined semantic layer, fine-tunes LLMs based on this layer, generates queries from LLM outputs, and verifies them against database results to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs are used to generate natural language responses, then ease of operation and user convenience are improved, but reliability and accuracy deteriorate due to hallucinations and false answers
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between the LLM and the user. This system includes a semantic layer that translates LLM outputs into structured queries, executes these queries against a knowledge base, and verifies the accuracy of responses. The intermediary layer filters out hallucinations while preserving the natural language interface benefits.
Solution Approach 2:
The patent implements a feedback mechanism where the output of the LLM is continuously verified against ground truth data from a knowledge base. When discrepancies are detected (hallucinations), the system provides feedback by either correcting the response or flagging it for review. This closed-loop feedback system improves reliability without compromising ease of use.
2Productivity
If LLMs generate answers without verification, then productivity and response speed are improved, but loss of information and accuracy deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-processing the knowledge base into a structured semantic layer before LLM queries are generated. This pre-organization of data enables rapid verification of LLM outputs without sacrificing accuracy. The semantic layer is built in advance with proper indexing and relationships, allowing fast query execution during runtime.
3Reliability
If a verification system is added to check LLM outputs, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the verification system into distinct modular components: a semantic layer module for translating queries, a query execution module for running queries against the knowledge base, and a verification module for comparing LLM outputs with ground truth. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity despite the added verification functionality.
4Manufacturing precision
If LLMs are fine-tuned on specific data, then manufacturing precision and model accuracy are improved, but loss of time and training resources increase
Solution Approach 1:
The patent applies local quality by fine-tuning the LLM on domain-specific data from the knowledge base rather than performing comprehensive retraining. This targeted fine-tuning focuses computational resources on the specific domain where accuracy is most critical, achieving high model accuracy while minimizing training time and resource consumption. The semantic layer also pre-processes data locally to match the fine-tuned model's expectations.
Data Source
AI summary
Methods and systems are disclosed for improving the reliability of large language model (LLM) outputs by mapping data from multiple data sources into a unified semantic layer and fine-tuning the LLM based on the semantic layer. An input prompt is processed by the fine-tuned LLM to generate an initial output answer. This output is automatically validated by generating and executing a database query derived from the output answer. The final validated answer is presented when the initial answer and query results match within a predefined threshold, reducing false or hallucinated responses. Practical applications include enhanced cybersecurity monitoring, automated threat investigation, and tenant-specific response generation in multi-tenant environments.