XML Interpreter for LLM Task Validation and Error Correction
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Solution Overview
Problem
Existing artificial intelligence tools struggle to autonomously navigate and perform tasks within structured software environments without extensive manual guidance, hindering their seamless integration and efficiency.
Innovation Solution
A system that utilizes a large language model (LLM) to generate computer-readable instructions, which are validated and executed by an AI assistant to perform tasks in a controlled environment, with the ability to correct errors and adapt to changes in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing AI tools are used to automate tasks, then productivity is improved, but the tools lack autonomous navigation capability in structured software environments
Solution Approach 1:
The patent introduces an XML-based intermediary language that serves as a bridge between natural language instructions and executable code. The LLM generates XML tags that encode structured navigation instructions, which are then interpreted by a dedicated XML interpreter to control the AI assistant's actions in the software environment, enabling autonomous navigation without direct human guidance
Solution Approach 2:
The system segments the task execution process into distinct phases: natural language input, XML tag generation, XML validation, and executable code execution. This segmentation allows each component to specialize in its function, with the XML structure providing a standardized framework for representing navigation and interaction steps independently
2Adaptability or versatility
If LLM generates code to perform tasks, then adaptability is improved, but code execution errors occur
Solution Approach 1:
The patent implements a feedback mechanism where the system executes the generated code and monitors for errors. When execution errors occur, the error information is fed back to the LLM, which generates corrected XML tags. This iterative feedback loop continues until the code executes successfully, ensuring reliable task completion while maintaining adaptability
Solution Approach 2:
The system performs preliminary validation of XML tags against a schema before execution, and prepares error handling mechanisms in advance. The XML structure itself serves as a preliminary check, ensuring that generated code follows the correct format and structure before being executed in the target environment
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.