Custom Interpreter for LLM Code Execution
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Solution Overview
Problem
Existing computer code interpreters face issues with security, efficiency, and flexibility due to their rigid nature of defining types upfront, leading to potential data exposure, stability problems, and inefficient resource utilization.
Innovation Solution
A custom interpreter system is developed to execute computer code generated by neural networks, featuring pre-runtime and runtime type checking, swappable logic, and state persistence, allowing for dynamic type exposure and improved adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If types are predefined upfront during initialization, then type checking can be performed before execution, but security is compromised due to exposure of type data and stability issues arise when functions or objects do not exist in predefined types
Solution Approach 1:
The patent implements dynamic type definition where types are not fixed upfront but are defined and exposed only when needed during code execution. This dynamic approach allows the interpreter to maintain security by not exposing type data prematurely while still performing necessary type checks at the appropriate time, resolving the contradiction between stability and security.
2Reliability
If types are fully defined upfront, then type checking is performed before execution, but computing resources are wasted on compiling and recompiling code for each update
Solution Approach 1:
The patent performs preliminary type checking only for the specific types and functions that are actually needed for the current code execution, rather than checking all predefined types upfront. This selective preliminary action maintains type checking accuracy for relevant code while avoiding the overhead of compiling and checking unrelated types, thereby improving compilation efficiency.
3Device complexity
If types are rigidly fixed upfront, then the interpreter structure is simple, but flexibility is reduced and resources are allocated for types that are never called
Solution Approach 1:
The patent implements dynamic type exposure where the interpreter structure remains relatively simple but gains flexibility through the ability to define and expose types dynamically during execution. Types are allocated and made available only when they are actually needed by the code, allowing the system to adapt to different code requirements without maintaining a complex predefined type structure for all possible types.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating context engine outputs by utilizing an interpreter purpose-built to execute code generated by large language models. For example, the disclosed systems generate computer code executable for responding to a query by utilizing a large language model. In addition, the disclosed systems execute the model-generated computer code utilizing an interpreter integrated with the context engine that further includes swappable logic interchangeable across multiple executors. Moreover, the disclosed systems can further generate as part of executing the computer code utilizing the interpreter, a first context engine output by implementing the interpreter at a first executor. Additionally, the disclosed systems can further generate a second context engine output by implementing the interpreter at a second executor.