LayoutPrompter: Awaken the Design Ability of Large Language Models

TL;DR

LayoutPrompter leverages large language models with in-context learning to generate high-quality graphic layouts from user constraints, outperforming traditional methods without training or fine-tuning.

Contribution

The paper introduces LayoutPrompter, a versatile, training-free approach that uses LLMs for graphic layout generation with dynamic exemplar selection and layout ranking.

Findings

Outperforms state-of-the-art on multiple layout tasks

Effective in low-data regimes

No model training or fine-tuning required

Abstract

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.