LayoutPrompter: Awaken the Design Ability of Large Language Models
Jiawei Lin Jiaqi Guo Shizhao Sun Zijiang James Yang Jian-Guang Lou Dongmei Zhang
Conditional graphic layout generation, which automatically maps user
constraints to high-quality layouts, has attracted widespread attention today.
Although recent works have achieved promising performance, the lack of
versatility and data efficiency hinders their practical applications. In this
work, we propose LayoutPrompter, which leverages large language models (LLMs)
to address the above problems through in-context learning. LayoutPrompter is
made up of three key components, namely input-output serialization, dynamic
exemplar selection and layout ranking. Specifically, the input-output
serialization component meticulously designs the input and output formats for
each layout generation task. Dynamic exemplar selection is responsible for
selecting the most helpful prompting exemplars for a given input. And a layout
ranker is used to pick the highest quality layout from multiple outputs of
LLMs. We conduct experiments on all existing layout generation tasks using four
public datasets. Despite the simplicity of our approach, experimental results
show that LayoutPrompter can compete with or even outperform state-of-the-art
approaches on these tasks without any model training or fine-tuning. This
demonstrates the effectiveness of this versatile and training-free approach. In
addition, the ablation studies show that LayoutPrompter is significantly
superior to the training-based baseline in a low-data regime, further
indicating the data efficiency of LayoutPrompter. Our project is available at
https://github.com/microsoft/LayoutGeneration/tree/main/LayoutPrompter.