1. Introduction
Large Language Models have demonstrated remarkable abilities in text generation and understanding, yet their capacity for visual-spatial reasoning and design comprehension, especially in complex applications like PowerPoint, remains underexplored. Existing benchmarks often fall short in assessing these specialized skills, leading to a gap in holistic evaluation. This work introduces PPTBench, a new benchmark and evaluation framework, to address this critical need. The primary model used in this article for evaluation is the PPTBench framework itself, which facilitates the assessment of various Large Language Models (LLMs).
2. Related Work
Prior research has explored multimodal LLMs and their application in image understanding and document processing, but specific benchmarks for PowerPoint layout and design are scarce. Studies on visual question answering and graphic design generation provide foundational context, often focusing on static image elements rather than the dynamic and hierarchical nature of presentation slides. Our work builds upon these foundations by creating a domain-specific evaluation tool tailored for presentation design.
3. Methodology
PPTBench is constructed from a diverse dataset of real-world PowerPoint slides, annotated with detailed layout information, design principles, and user intent. The benchmark includes tasks such as layout prediction, design critique, element rearrangement, and content-to-slide mapping, designed to probe different facets of design understanding. Performance is measured using both quantitative metrics, such as accuracy and F1-score for classification tasks, and qualitative assessments for generation tasks, ensuring a comprehensive evaluation of LLMs.
4. Experimental Results
Our experiments with several state-of-the-art Large Language Models on PPTBench reveal varying levels of performance across different design tasks. While models exhibit reasonable proficiency in simple layout recognition, they struggle significantly with nuanced design critiques and complex spatial manipulations requiring deeper contextual understanding. The table below summarizes the average performance of three representative LLMs across key PPTBench tasks, highlighting the current capabilities and limitations.
The experimental results demonstrate that existing LLMs, while capable of basic design understanding, exhibit notable weaknesses in advanced layout and design tasks. For instance, LLM C performs best on layout recognition, but all models show low accuracy in complex rearrangement and design critique. This indicates a significant gap in their ability to reason about spatial relationships and aesthetic principles within a presentation context.
| LLM Model | Layout Recognition Accuracy (%) | Element Rearrangement F1-score | Design Critique Score (out of 5) |
|---|---|---|---|
| LLM A | 72.5 | 0.45 | 2.8 |
| LLM B | 68.1 | 0.39 | 2.5 |
| LLM C (State-of-the-Art) | 78.9 | 0.51 | 3.1 |
5. Discussion
The results underscore the need for further research into LLMs capable of sophisticated visual-spatial reasoning and aesthetic understanding for design applications. Current models, despite their scale, often treat design elements as disconnected components rather than parts of a coherent visual structure. Future work should focus on integrating stronger perception modules and training paradigms that emphasize hierarchical design principles and human-like aesthetic judgments to bridge this performance gap.