Empowering LLMs for Multi-Page Layout Generation via Consistency-Oriented In-Context Learning

Document layout generation, a burgeoning field of document intelligence, entails positioning and sizing various elements within given constraints. While significant strides have been made in single-page layout generation, real-world documents predominantly span multiple pages, and exploring multi-page layout generation methods has also become the key to meeting the contemporary dramatically increased document processing demands. Despite the promise of leveraging large language models (LLMs) like GPT-4 for their powerful in-context learning abilities, the task transition to multi-page layouts, which contains considerably complex data, presents formidable challenges including excessively long prompts and strict consistency between pages. To this end, we propose a novel framework called Multi-Page Layout Generation via Consistency-Oriented modeling (MuLCO) that capitalizes on in-context learning of LLMs without the need for training or fine-tuning. MuLCO employs three key components: serialization based on code blocks maps intricate document layouts to code-style exemplars, self-correcting reasoning hint decomposes the complex generation task into numerous steps to improve reasoning interpretability, and consistency-oriented multi-round generation predicts coherent multi-page layouts in form of a continuous dialogue. To summarize, we contribute by proposing MuLCO and developing a task-specific dataset and evaluation mechanism. Extensive experiments validate the effectiveness of the MuLCO framework for multi-page layout generation.