title: LayoutPrompter: Awaken the Design Ability of Large Language Models
summary: 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.
paper_authors._id: 6552ec07b055d2c5437f4608
paper_authors.name: Jiawei Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-14T10:56:56.811Z
paper_authors._id: 6440dda9cea37249a0f9b473
paper_authors.avatarUrl: /avatars/9747e1ca11ed5725b2b9968f028cac93.svg
paper_authors.fullname: Jiawei Lin
paper_authors.type: user
paper_authors.user: KyleLin
paper_authors._id: 6552ec07b055d2c5437f4609
paper_authors.name: Jiaqi Guo
paper_authors._id: 6552ec07b055d2c5437f460a
paper_authors.name: Shizhao Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-14T10:57:17.710Z
paper_authors._id: 63eb00a191a1b8ec4fbba2a9
paper_authors.avatarUrl: /avatars/0cc7cf9b6d05337603f700e0d592edf5.svg
paper_authors.fullname: ShizhaoSun
paper_authors.type: user
paper_authors.user: ShizhaoSun
paper_authors._id: 6552ec07b055d2c5437f460b
paper_authors.name: Zijiang James Yang
paper_authors._id: 6552ec07b055d2c5437f460c
paper_authors.name: Jian-Guang Lou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-14T10:57:55.893Z
paper_authors._id: 6358cc2bf56b03ec91484b99
paper_authors.avatarUrl: /avatars/7a7f3be7c5f1a209d4d68bba206259d6.svg
paper_authors.fullname: Luo Jianguang
paper_authors.type: user
paper_authors.user: luojianguang
paper_authors._id: 6552ec07b055d2c5437f460d
paper_authors.name: Dongmei Zhang
paper_title: LayoutPrompter: Awaken the Design Ability of Large Language Models
paper_summary: 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.
paper_ai_summary: LayoutPrompter uses large language models and in-context learning to generate high-quality layouts with versatility and data efficiency.
paper_ai_keywords: large language models in-context learning input-output serialization dynamic exemplar selection layout ranking