PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation
This benchmark addresses the critical bottleneck of coarse-grained evaluation in automated slide generation for researchers and developers.
The paper introduces PresentBench, a new benchmark for evaluating automated slide generation, which includes 238 evaluation instances and an average of 54.1 binary checklist items per instance. This benchmark provides more reliable evaluation results and stronger alignment with human preferences compared to existing methods, revealing that NotebookLM significantly outperforms other slide generation methods.
Slides serve as a critical medium for conveying information in presentation-oriented scenarios such as academia, education, and business. Despite their importance, creating high-quality slide decks remains time-consuming and cognitively demanding. Recent advances in generative models, such as Nano Banana Pro, have made automated slide generation increasingly feasible. However, existing evaluations of slide generation are often coarse-grained and rely on holistic judgments, making it difficult to accurately assess model capabilities or track meaningful advances in the field. In practice, the lack of fine-grained, verifiable evaluation criteria poses a critical bottleneck for both research and real-world deployment. In this paper, we propose PresentBench, a fine-grained, rubric-based benchmark for evaluating automated real-world slide generation. It contains 238 evaluation instances, each supplemented with background materials required for slide creation. Moreover, we manually design an average of 54.1 checklist items per instance, each formulated as a binary question, to enable fine-grained, instance-specific evaluation of the generated slide decks. Extensive experiments show that PresentBench provides more reliable evaluation results than existing methods, and exhibits significantly stronger alignment with human preferences. Furthermore, our benchmark reveals that NotebookLM significantly outperforms other slide generation methods, highlighting substantial recent progress in this domain.