LLM Context Enhancement via Structured Data Mediator
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Solution Overview
Problem
Current computer systems struggle to deeply understand and process natural language, leading to limitations in handling heterogeneous and broad data sets required for complex applications, such as health management and accounting, where existing methods rely on structured data or statistical language models that are unreliable and lack explainability.
Innovation Solution
A method involving a structured, machine-readable representation of data, such as a universal language, is used to interact with large language models (LLMs), enabling improved output generation, validation of natural language for factual accuracy, and avoidance of hallucination, by providing structured context and training data to enhance LLM performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If structured data is used to store information for processing, then data processing reliability is improved, but the application cannot handle heterogeneous and broad data sets required for complex applications
Solution Approach 1:
The patent introduces natural language as an intermediary layer between structured data and application processing. The system translates structured data into natural language representations, allowing applications to process heterogeneous data through language-based interfaces while maintaining the reliability of structured storage. This mediator enables both structured reliability and unstructured versatility.
Solution Approach 2:
The system combines structured data formats with natural language processing capabilities to create a composite data handling approach. By integrating both structured storage mechanisms and unstructured language processing, the system achieves both reliability and adaptability simultaneously, rather than choosing one over the other.
2Device complexity
If a limited schema is used to manage data, then application development complexity is reduced, but the application becomes narrow and cannot cover the full range of required properties
Solution Approach 1:
The patent implements a universal natural language interface that can handle multiple types of data and applications through a single schema-agnostic system. Instead of creating specialized schemas for different applications, the system uses language-based processing that works across diverse data types, making the application both simple to develop and broadly applicable.
Solution Approach 2:
The system employs dynamic natural language processing that can adapt to different data types and application requirements in real-time, rather than relying on static predefined schemas. This allows the application to handle diverse properties without requiring complex predetermined structures.
3Productivity
If statistical natural language processing techniques are used, then processing capability is improved, but the system lacks real understanding and becomes inherently unreliable
Solution Approach 1:
The system uses self-service mechanisms where the natural language processing enhances rather than replaces structured data processing. The language models serve themselves by operating within constraints provided by reliable structured data, ensuring that productivity gains do not compromise understanding reliability.
Data Source
AI summary
Methods are provided, such as a method of interacting with a large language model (LLM), including the step of a processing system using a structured, machine-readable representation of data that conforms to a machine-readable language, such as a universal language, to provide new context data for the LLM, in order to improve the output, such as continuation text output, generated by the LLM in response to a prompt; and such as a method of interacting with a LLM, including the step of providing continuation data generated by the LLM to a processing system that uses a structured, machine-readable representation of data that conforms to a machine-readable language, such as a universal language, in which the processing system is configured to analyse the continuation output generated by the LLM in response to a prompt to enable an improved version of that continuation output to be provided to a user. Related computer systems are provided.