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