SlidesGen-Bench: Evaluating Slides Generation via Computational and Quantitative Metrics

TL;DR

SlidesGen-Bench introduces a comprehensive, quantitative evaluation framework for slide generation systems, emphasizing universality, reproducibility, and alignment with human preferences, to address the challenges of assessing diverse LLM-based slide creation methods.

Contribution

It presents a unified, visual domain-based benchmark with quantitative metrics and a human-aligned dataset to evaluate slide generation systems more reliably.

Findings

Higher correlation with human preferences than existing methods

Quantitative assessment across Content, Aesthetics, and Editability

Effective evaluation across nine slide generation systems

Abstract

The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts to image-centric synthesis. However, evaluating these heterogeneous systems remains challenging, as existing protocols often struggle to provide comparable scores across architectures or rely on uncalibrated judgments. In this paper, we introduce SlidesGen-Bench, a benchmark designed to evaluate slide generation through a lens of three core principles: universality, quantification, and reliability. First, to establish a unified evaluation framework, we ground our analysis in the visual domain, treating terminal outputs as renderings to remain agnostic to the underlying generation method. Second, we propose a computational approach that quantitatively assesses slides across three distinct dimensions - Content, Aesthetics, and Editability - offering reproducible metrics where prior works relied on subjective or reference-dependent proxies. Finally, to ensure high correlation with human preference, we construct the Slides-Align1.5k dataset, a human preference aligned dataset covering slides from nine mainstream generation systems across seven scenarios. Our experiments demonstrate that SlidesGen-Bench achieves a higher degree of alignment with human judgment than existing evaluation pipelines. Our code and data are available at https://github.com/YunqiaoYang/SlidesGen-Bench.