LayoutPrompter：喚醒大型語言模型的設計能力

LayoutPrompter: Awaken the Design Ability of Large Language Models

November 11, 2023

作者: Jiawei Lin, Jiaqi Guo, Shizhao Sun, Zijiang James Yang, Jian-Guang Lou, Dongmei Zhang

cs.AI

摘要

條件圖形佈局生成，自動將用戶約束映射到高質量佈局，如今已引起廣泛關注。儘管最近的研究取得了令人期待的表現，但缺乏通用性和數據效率阻礙了它們的實際應用。在這項工作中，我們提出了LayoutPrompter，它利用大型語言模型（LLMs）通過上下文學習來解決上述問題。LayoutPrompter由三個關鍵組件組成，即輸入輸出序列化、動態示例選擇和佈局排名。具體而言，輸入輸出序列化組件精心設計了每個佈局生成任務的輸入和輸出格式。動態示例選擇負責為給定輸入選擇最有幫助的提示示例。佈局排名器用於從LLMs的多個輸出中選擇最高質量的佈局。我們使用四個公共數據集對所有現有的佈局生成任務進行實驗。儘管我們方法的簡單性，實驗結果表明，LayoutPrompter在這些任務上可以與甚至優於最先進的方法，而無需進行任何模型訓練或微調。這表明了這種通用且無需訓練的方法的有效性。此外，消融研究表明，在低數據情況下，LayoutPrompter明顯優於基於訓練的基線，進一步表明了LayoutPrompter的數據效率。我們的項目可在https://github.com/microsoft/LayoutGeneration/tree/main/LayoutPrompter找到。

English

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.