Visual Program Unit Testing for Reliable Reasoning
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Solution Overview
Problem
Existing visual programming systems generate non-transferable and unreliable code due to flawed underlying logic, despite producing correct outputs on training data, leading to difficulties in generating good training data and ensuring code reliability.
Innovation Solution
Utilize a Large Language Model (LLM) to automatically generate unit tests with image descriptions and expected answers, apply these tests to multiple programs, and select the highest scoring program, with reinforcement learning to improve model performance and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised methods are used to improve visual program synthesis performance, then the model can leverage training data to generate correct outputs, but the underlying logic may be flawed leading to non-transferable and unreliable code
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate programs before selecting the final one. Instead of directly generating a single program from training data, the model creates several candidate solutions and evaluates them against unit tests, ensuring that the selected program has sound underlying logic and will transfer reliably to new tasks.
Solution Approach 2:
The system implements feedback mechanisms by using unit tests to evaluate candidate programs. The results of these tests provide feedback that guides the selection process, allowing the system to identify and select programs with correct logic rather than merely those that happen to produce correct outputs on training data.
2Adaptability or versatility
If a language model generates code to answer visual questions, then the system can handle compositional reasoning tasks, but the generated code may produce correct outputs for incorrect reasons
Solution Approach 1:
The system generates multiple candidate programs as preliminary actions before final selection. This allows the model to explore different reasoning approaches and select the one with correct logic, rather than committing to the first generated solution that may have flawed reasoning despite producing correct outputs.
Solution Approach 2:
Unit tests provide feedback on the reasoning correctness of candidate programs. By evaluating each candidate against carefully designed unit tests that check for correct reasoning patterns, the system can identify programs that produce correct outputs for incorrect reasons and eliminate them in favor of programs with sound logic.
3Reliability
If multiple programs are generated and evaluated with unit tests, then the reliability of the selected program improves, but the computational complexity and time required increases
Solution Approach 1:
The system applies partial action by generating a limited but sufficient number of candidate programs rather than exhaustively searching all possible programs. This generates enough candidates to ensure reliable selection while avoiding excessive computational overhead. The unit test evaluation is also applied selectively to focus on discriminating between correct and incorrect reasoning patterns.
Solution Approach 2:
The system performs preliminary filtering of candidate programs before full evaluation. By quickly assessing candidates and eliminating obviously incorrect ones, the system reduces the number of programs that require comprehensive unit test evaluation, thereby reducing overall evaluation time while maintaining reliability.
Data Source
AI summary
Embodiments described herein provide for utilizing a large language model (LLM) to automatically generate unit tests, comprising image descriptions and expected answers for specified queries for use in visual programming. Further, text-to-image generation models are utilized to create images that align with the descriptions provided in each unit test. In some embodiments, a system executes only the top-scoring programs, reverts to a baseline model in cases of low scores, uses unit tests for re-prompting, and/or applies unit tests in reinforcement learning scenarios.