LLM XML Interpreter With Feedback Error Correction
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Solution Overview
Problem
Existing artificial intelligence tools struggle to autonomously navigate and interact with structured software environments without extensive manual guidance, hindering their ability to perform tasks seamlessly and efficiently.
Innovation Solution
A system that integrates a large language model (LLM) to generate computer-readable instructions based on natural language inputs, executes these instructions, and validates the output to ensure accurate task completion, using a structured workflow to interact with environments like data management platforms or manufacturing facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing AI tools are used without extensive manual guidance, then automation capability is improved, but task execution accuracy deteriorates due to inability to autonomously navigate structured software environments
Solution Approach 1:
The patent introduces an XML interpreter as an intermediary component between the LLM and the execution environment. This mediator validates and corrects the code generated by the LLM, ensuring that the automated tasks are executed accurately. The interpreter acts as a bridge that translates the LLM's natural language understanding into precise, error-free code execution, thereby maintaining both high automation capability and task execution accuracy.
2Productivity
If LLM generates code autonomously, then productivity is improved, but code quality deteriorates due to lack of validation and error correction
Solution Approach 1:
The patent implements a feedback mechanism where the XML interpreter validates the code generated by the LLM and provides corrections back to the system. This feedback loop ensures that code quality is maintained by automatically detecting and correcting errors, while the overall productivity remains high because the validation process is automated and occurs rapidly without requiring manual intervention.
3Ease of operation
If manual guidance is reduced for AI tools, then ease of operation is improved, but device complexity increases due to need for autonomous navigation capabilities
Solution Approach 1:
The patent segments the autonomous navigation capability into distinct modular components: the LLM for understanding natural language instructions, the XML interpreter for validating and correcting code, and the execution environment for running tasks. This segmentation allows each component to specialize in a specific function, reducing the overall complexity while maintaining ease of operation, as users only need to provide high-level natural language instructions without needing to understand the internal complexity of each module.
Data Source
AI summary
A system implements an interpreter for extensible markup language (XML) to validate XML produced by large language models (LLMs). The system uses an LLM to generate a first set of XML instructions to perform a first task in an environment communicatively coupled to the system. The system executes the first set of XML instructions and observes a result of the execution. Based on the observed result, the system either uses the LLM to generate a second set of XML instructions to perform a second task, or detects an error in the first set of XML instructions. When an error is detected, the system uses the LLM to modify the first set of XML instructions to correct the error.