Textual-to-Visual Iterative Self-Verification for Slide Generation

ACL ARR 2025 February Submission7150 Authors

Abstract:

Generating presentation slides is a time-consuming task that urgently requires automation.Due to their limited flexibility and lack of automated refinement mechanisms, existing autonomous LLM-based agents face constraints in real-world applicability.

In this work, we decompose the task of generating missing presentation slides into two key components: content generation and layout generation, aligning with the typical process of creating academic slides. For content generation, we introduce a content generation approach that enhances coherence and relevance by incorporating context from surrounding slides and leveraging section retrieval strategies. For layout generation, we propose a textual-to-visual self-verification process using a LLM-based Reviewer + Refiner workflow, transforming complex textual layouts into intuitive visual formats. This modality transformation simplifies the task, enabling accurate and human-like review and refinement.
Experiments show that our approach significantly outperforms baseline methods in terms of alignment, logical flow, visual appeal, and readability.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: LLM Agent, academic applications, slide generation

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 7150