Controlled LLM Code Execution for Safe Natural Language Automation
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Solution Overview
Problem
Foundation models (FMs) require a supervised environment for code execution, limiting their accessibility and usability for non-engineers, as they need terminal interaction and manual supervision to ensure proper functioning.
Innovation Solution
A controlled Large Language Model (LLM) Code Interpreter and Execution Environment (LCIEE) that generates and executes code safely, using a Limited Python (LP) executer to manage errors and access external resources, providing a user-friendly interface for natural language-based task automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If FMs are used to generate code from natural language requests, then code generation capability is improved, but the system requires terminal interaction and manual supervision which reduces ease of operation
Solution Approach 1:
The patent introduces a controlled execution environment that acts as an intermediary between the foundation model and the user. This environment automatically handles code execution, error management, and resource access without requiring terminal interaction or manual supervision from users, thereby maintaining code generation capability while significantly improving ease of operation for non-engineers
Solution Approach 2:
The system implements self-service mechanisms where the controlled execution environment automatically manages code execution, detects errors, and accesses external resources without human intervention. The foundation model generates code that is then autonomously executed and monitored within the controlled environment, eliminating the need for manual terminal operations and supervision
2Reliability
If FMs require supervised environment for code execution, then execution safety is improved, but accessibility and usability are reduced
Solution Approach 1:
The patent segments the system into distinct components: a foundation model for code generation and a controlled execution environment for safe code execution. This segmentation allows the execution safety functions to be isolated and managed separately, while the generated code can be accessed and executed by a broader audience without compromising safety
Solution Approach 2:
The controlled execution environment serves as an intermediary layer that maintains execution safety while improving accessibility. It automatically manages the execution process, error handling, and resource access, allowing non-engineers to safely use the system without needing to understand or manage the safety mechanisms themselves
3Reliability
If manual supervision is required to ensure proper functioning, then execution reliability is improved, but productivity is reduced due to time-consuming manual processes
Solution Approach 1:
The system implements self-service automation where the controlled execution environment automatically supervises code execution, detects errors, and manages resource access without requiring manual intervention. This maintains execution reliability through automated monitoring while significantly improving productivity by eliminating time-consuming manual processes
Solution Approach 2:
The system incorporates automated feedback mechanisms where the controlled execution environment continuously monitors code execution, detects errors in real-time, and provides feedback to the foundation model for iterative improvement. This automated feedback loop maintains execution reliability while improving productivity by eliminating manual supervision requirements
Data Source
AI summary
Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.