AdGPT: Explore Meaningful Advertising with ChatGPT

Jiannan Huang, Mengxue Qu, Longfei Li, Yunchao Wei

Abstract: Advertising is pervasive in everyday life. Some advertisements are not as readily comprehensible, as they convey a deeper message or purpose, which is referred to as “meaningful advertising.” These ads often aim to create an emotional connection with the audience or promote a social cause. Developing a method for automatically understanding meaningful advertising would be advantageous for the dissemination and creation of such ads. However, current models of ad understanding primarily focus on the superficial aspects of images. In this article, we introduce AdGPT, a model that leverages visual expert analysis to guide Large Language Models (LLMs) in generating adaptive reasoning chains. Informed by these chains of thought, the model can intelligently comprehend meaningful ads regarding category, content, and sentiment. To assess the effectiveness of our approach, we extract a subset of meaningful ads from the widely used Pitt’s ad images for analysis. Beyond employing traditional ad understanding metrics to evaluate the LLMs’ comprehensive ad comprehension, we also develop a novel generative metric that aligns with user study evaluations for consistent performance assessment. Experiments show that our methods outperform existing state-of-the-art (SOTA) approaches directly linking visual expert models and LLMs and large-scale visual-language models. Code is available at https://github.com/Rbrq03/AdGPT.

External IDs:dblp:journals/tomccap/HuangQLW25