Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains labor-intensive. Existing automation methods mostly follow fixed template filling and cannot support dynamic updates for diverse, user-authored slide decks. We therefore define "Dynamic Slide Update via Natural Language Instructions on User-provided Templates" and introduce DynaSlide, a large-scale benchmark with 20,036 real-world instruction-execution triples (source slide, user instruction, target slide) grounded in a shared external database and built from business reporting slides under bring-your-own-template (BYO-template) conditions. To tackle this task, we propose SlideAgent, an agent-based framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions. SlideAgent updates content while preserving layout and style, providing a strong reference baseline on DynaSlide. We further design end-to-end and component-level evaluation protocols that reveal key challenges and opportunities for future research. The dataset and code are available at https://github.com/XiaoZhou2024/SlideAgent.
@article{arxiv.2604.17894,
title = {Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions},
author = {Kun Zhou and Jiakai He and Wenmian Yang and Zhensheng Wang and Yiquan Zhang and Weijia Jia},
journal= {arXiv preprint arXiv:2604.17894},
year = {2026}
}
Comments
To appear in Findings of the Association for Computational Linguistics (ACL 2026)