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Computer Science > Computation and Language

arXiv:2410.12844 (cs)
[Submitted on 9 Oct 2024]

Title:TextLap: Customizing Language Models for Text-to-Layout
Planning

Authors:Jian Chen, Ruiyi Zhang, Yufan Zhou, Jennifer Healey, Jiuxiang
Gu, Zhiqiang Xu, Changyou Chen
View a PDF of the paper titled TextLap: Customizing Language Models
for Text-to-Layout Planning, by Jian Chen and 6 other authors
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    Abstract:Automatic generation of graphical layouts is crucial for
    many real-world applications, including designing posters,
    flyers, advertisements, and graphical user interfaces. Given the
    incredible ability of Large language models (LLMs) in both
    natural language understanding and generation, we believe that we
    could customize an LLM to help people create compelling graphical
    layouts starting with only text instructions from the user. We
    call our method TextLap (text-based layout planning). It uses a
    curated instruction-based layout planning dataset (InsLap) to
    customize LLMs as a graphic designer. We demonstrate the
    effectiveness of TextLap and show that it outperforms strong
    baselines, including GPT-4 based methods, for image generation
    and graphical design benchmarks.

Comments: Accepted to the EMNLP Findings
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2410.12844 [cs.CL]
          (or arXiv:2410.12844v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2410.12844
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jian Chen [view email]
[v1] Wed, 9 Oct 2024 19:51:38 UTC (5,036 KB)
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