PropTest: Automatic Property Testing for Improved Visual Programming

Understand

Visual Programming has recently emerged as an alternative to end-to-end black-box visual reasoning models.

• This type of method leverages Large Language Models (LLMs) to generate the source code for an executable computer program that solves a given problem.
• This strategy has the advantage of offering an interpretable reasoning path and does not require finetuning a model with task-specific data.
• We propose PropTest, a general strategy that improves visual programming by further using an LLM to generate code that tests for visual properties in an initial round of proposed solutions.