Can Large Language Models Understand Symbolic Graphics Programs?

Abstract

Assessing the capabilities of large language models (LLMs) is important to be able to characterize what they know (and don’t) and how they can be appropriately used. Yet, capability assessments are often challenging, in part, because it is hard to find tasks to which they have not been exposed during training. We take one step to address this challenge by turning to a new task: focusing in on symbolic graphics programs, which are a popular representation for graphics content that procedurally generates visual data. Large language models (LLMs) have shown exciting promise towards program synthesis, but do they “understand” symbolic graphics programs? Unlike conventional programs, symbolic graphics programs can be translated to graphics content (e.g., 2D images, 3D geometry). Here, we characterize an LLM’s “understanding” of symbolic programs in terms of their ability to answer questions related to the graphics (spatial) content. This task is challenging as the questions are difficult to answer from the symbolic programs alone – yet, they would be easy to answer from the corresponding graphics content as we verify through a human experiment. To understand symbolic programs, LLMs may need to possess the ability to “imagine” how the corresponding graphics content would look without directly accessing the rendered visual content. We use this task to evaluate LLMs by creating a large benchmark for the semantic understanding of symbolic graphics programs. This benchmark is built via a novel usage of program-graphics correspondence, hence requiring minimal human efforts. We evaluate both commercial and open-source LLMs on our benchmark to elucidate a preliminary assessment of their ability to reason about visual scenes from programs. We find that this task well distinguishes existing LLMs and models that are considered good at reasoning perform better. Lastly, we introduce a way to improve this ability – Symbolic Instruction Tuning (SIT). Specifically, we query powerful vision-language models (e.g., GPT-4o) with questions and images generated by symbolic programs. These program-question pairs are collected as our instruction dataset which is then used to finetune an LLM. With a small amount of data, we find that SIT can improve the understanding of LLMs regarding symbolic graphics programs. Assessing how well models “understand” symbolic graphics programs offers new possibilities for LLMs to perform visual reasoning. Finally, we showcase such possibilities in generic instruction tuning.