LLM Data Model Interoperability Through Semantic Fine-Tuning
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Solution Overview
Problem
Existing enterprise information systems are suboptimal for modern Large Language Models (LLMs) due to factors like ambiguous entity names, unclear annotations, and imprecise relationship definitions, leading to poor model performance, increased implementation costs, and delayed innovation, as they were not designed to support the requirements of modern LLMs and Generative AI (GenAI) technologies.
Innovation Solution
A system and method for assessing and improving data interoperability by receiving metadata from data sources, determining interpretability levels using predefined criteria, generating performance reports, applying semantic modifications, and fine-tuning data models with Generative Artificial Intelligence (GenAI) to enhance data structure and quality, integrating with GenAI applications, and updating database schemas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing enterprise information systems are used without modification, then system stability and legacy compatibility are maintained, but data interoperability with LLMs and GenAI technologies deteriorates due to ambiguous entity names, unclear annotations, and imprecise relationship definitions
Solution Approach 1:
The system performs preliminary assessment of data models against LLM readiness criteria before integration, identifying ambiguities and deficiencies in advance. This allows organizations to proactively improve their data models through semantic modifications and fine-tuning, ensuring better interoperability with GenAI applications while maintaining system stability.
Solution Approach 2:
The patent introduces an intermediary assessment system that acts as a bridge between legacy enterprise information systems and modern LLM/GenAI technologies. This intermediary evaluates data models, generates performance reports, and guides fine-tuning processes, enabling gradual adaptation without forcing immediate system replacement or disruption.
2Measurement precision
If data models are fine-tuned with semantic modifications to improve LLM interpretability, then model performance and data interoperability are improved, but implementation complexity and processing time increase due to dependency graph construction and iterative fine-tuning
Solution Approach 1:
The fine-tuning process is segmented into distinct phases: initial assessment, dependency graph construction, semantic modification identification, and iterative fine-tuning. Each phase produces specific outputs that feed into the next, making the complex process more manageable and allowing parallel processing of independent components.
Solution Approach 2:
The system implements feedback loops where performance reports from LLM evaluation inform subsequent semantic modifications, which are then re-evaluated to measure improvement. This iterative feedback process systematically reduces ambiguities and enhances interpretability while tracking progress toward performance targets.
3Manufacturing precision
If comprehensive assessment criteria including informativeness, ambiguity, completeness, relevance, and consistency are applied, then data model quality and LLM readiness are improved, but assessment time and computational resources increase
Solution Approach 1:
The assessment system allows organizations to apply the full five-criteria evaluation framework or select subsets based on specific needs and resource constraints. This partial action approach enables quicker assessments when comprehensive evaluation is not immediately necessary, while still providing the option for thorough evaluation when time and resources permit.
Data Source
AI summary
A system and a method for assessing and improving data interoperability of large language models (LLMs) are disclosed. The method includes receiving metadata associated with a data model, determining an interpretability level of the data model by the LLM, computing a performance score for each entity of the plurality of entities based on the determined interpretability level, generating a performance report including semantic attributes and deficiencies of the data model, determining semantic modifications to be performed to each of the plurality of entities, constructing a dependency graph for each entity to identify an impact of the at least one semantic modification on related entities, fine-tuning the data model with the at least one semantic modification based on the constructed dependency graph, integrating the fine-tuned data model with at least one Gen AI application, and updating database schemas corresponding to the fine-tuned data model based on the integrated Gen AI application.