Transformer Model Conversion to Human-Readable Programs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Transformer models are complex and opaque, making it difficult to understand the mechanisms behind their task performance, and conventional methods cannot convert them into human-readable programs, limiting the ability to analyze, optimize, or validate their functionality.
Innovation Solution
A method and system that generate mapping models to convert transformer models into human-readable programs by training on program-transformer pairs, using intermediate representations, and applying quality and inclusion preservation to ensure the generated programs reflect the transformer's functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If transformer models are used to achieve complex task performance, then task capability is improved, but interpretability and analyzability deteriorate due to model complexity and opacity
Solution Approach 1:
The patent creates a copy of the transformer model's functionality in the form of a simplified program representation. Instead of directly analyzing the complex transformer weights and architecture, the system generates an equivalent program that replicates the transformer's input-output behavior, making it analyzable while preserving task capability
Solution Approach 2:
The patent introduces an intermediate representation layer between the transformer model and the analysis process. This intermediate program format serves as a mediator that translates the opaque transformer operations into human-readable, analyzable code structure, enabling interpretation without losing the original model's functionality
2Productivity
If transformer models are used for task execution, then performance is improved, but the ability to convert into human-readable programs deteriorates due to architectural opacity
Solution Approach 1:
The patent replaces the mechanical/structural approach of directly inspecting transformer architecture with a functional substitution approach. Instead of trying to read the model structure, the system substitutes it with a program that performs the same function, making the behavior observable and analyzable in human-readable form
Solution Approach 2:
The patent changes the representation parameters of the model from numerical weights and matrix operations to symbolic program constructs. By transforming the parameter space from continuous floating-point values to discrete program statements, the system enables human readability while maintaining functional equivalence
3Measurement precision
If conventional methods are used to analyze transformer models, then analysis capability is limited, but model functionality cannot be validated due to lack of conversion methods
Solution Approach 1:
The patent implements a feedback mechanism where the generated program is validated against the original transformer model's behavior. The system compares inputs and outputs between the transformer and its program representation, providing feedback to ensure functional equivalence and enabling reliability validation that conventional methods cannot achieve
Data Source
AI summary
In variants, the method can include generating mapping model training data, determining the mapping model, and predicting a program based on a transformer. The method can optionally include evaluating the mapping model, running analyses on the program, and/or utilizing the program and/or generated program analyses. The method functions to convert transformer models into programs that can be characterized and/or analyzed using program analysis techniques.