PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation

2026-03-07

AI Summary

Existing evaluation methods for slide generation are overly coarse-grained and lack fine-grained, verifiable criteria, making it difficult to accurately assess model performance. To address this limitation, this work proposes PresentBench—the first fine-grained benchmark for slide generation grounded in real-world scenarios—comprising 238 evaluation instances with accompanying source materials. For each instance, human annotators designed an average of 54.1 binary judgment items, enabling a rubric-based, verifiable evaluation framework. Experimental results demonstrate that PresentBench exhibits strong alignment with human preferences and significantly outperforms existing evaluation approaches. Furthermore, the benchmark reveals that NotebookLM achieves notably strong performance on slide generation tasks.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Evaluation and Analysis
• Knowledge Representation and Reasoning: Preferences

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• slide generation
• evaluation benchmark
• fine-grained assessment
• rubric-based evaluation
• automated presentation

Innovation

• fine-grained evaluation
• rubric-based benchmark
• slide generation
• checklist-based assessment
• automated presentation