LLM Context Management via Structured Universal Data Representation
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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 data 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 improve the output of large language models by providing new context and enabling improved continuation text generation, validation of natural language for factual accuracy, and avoidance of hallucination, while ensuring original text generation and adding citations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If statistical language models are used to process natural language, then the system can handle unstructured text data, but the results are unreliable and lack explainability
Solution Approach 1:
The patent introduces an intermediary structured representation layer between unstructured natural language input and the processing system. Natural language text is converted into structured data representations (such as parsed semantic structures, extracted entities, and organized knowledge graphs) that serve as a bridge, enabling reliable processing while maintaining the ability to handle diverse natural language inputs.
2Reliability
If structured data is used to store information for processing, then the application can work reliably, but the schema required becomes enormous for heterogeneous data collections
Solution Approach 1:
The patent employs a universal structured representation format that can accommodate multiple types of heterogeneous data (text, numerical, categorical, hierarchical) within a single flexible schema. This universal structure uses standardized fields and relationships that can represent diverse data types without requiring separate complex schemas for each data category, thereby reducing overall system complexity while maintaining reliability.
3Ease of manufacture
If a limited schema is used in HUB applications, then the application can be built practically, but it cannot cover the full range of required data properties
Solution Approach 1:
The patent implements a dynamic schema system where the data structure can adapt and extend based on the specific application requirements. The structured representation includes flexible fields that can be added, modified, or configured at runtime, allowing the system to start with a simple practical schema and evolve to cover the full range of required data properties without requiring complete redesign.
4Adaptability or versatility
If natural language processing techniques are used to augment limited schemas, then the application can handle broader data, but the meaning of natural language names remains opaque to the system
Solution Approach 1:
The patent introduces structured semantic representations as an intermediary layer between natural language names and the processing system. Natural language terms are parsed and converted into structured data with explicit semantic meanings, relationships, and contextual information. This intermediary structured representation preserves the semantic meaning of natural language while making it machine-processable and transparent to the system.
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.