串联文本生成图像模型与大语言模型：一种生成个性化电商横幅的新方法
Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners
AI总结：
针对电商人工制作横幅效率低、扩展性差的问题，提出串联LLM与文本生成图像模型的方法，可自动从商品元信息提取属性生成提示词，无需人工干预即可生成高质量个性化电商横幅。
摘要：
Stable Diffusion等文本生成图像模型为艺术创作带来了大量机遇，近期研究已探讨这类模型对创意艺术家工作的赋能作用。当前许多电商平台仍采用人工流程制作横幅，既耗时又存在可扩展性限制。本研究验证了文本生成图像模型可用于为线上购物者生成基于其交互行为的动态内容个性化网页横幅。该方法的创新点在于无需人工干预，即可将用户交互数据转化为有效提示词。为此，我们利用大语言模型（LLM）从商品元信息中系统提取属性元组，再通过提示工程将这些属性输入文本生成图像模型，生成横幅所需图像。实验结果表明，所提方法能够为用户生成高质量的个性化横幅。
英文摘要：
Text-to-image models such as stable diffusion have opened a plethora of opportunities for generating art. Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists. Many e-commerce platforms employ a manual process to generate the banners, which is time-consuming and has limitations of scalability. In this work, we demonstrate the use of text-to-image models for generating personalized web banners with dynamic content for online shoppers based on their interactions. The novelty in this approach lies in converting users' interaction data to meaningful prompts without human intervention. To this end, we utilize a large language model (LLM) to systematically extract a tuple of attributes from item meta-information. The attributes are then passed to a text-to-image model via prompt engineering to generate images for the banner. Our results show that the proposed approach can create high-quality personalized banners for users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
