Large language models(LLM) such as ChatGPT have substantially simplified the generation of marketing copy, yet producing content satisfying domain specific requirements, such as effectively engaging customers, remains a significant challenge. In this work, we introduce the Genetic Copy Optimization Framework (GCOF) designed to enhance both efficiency and engagememnt of marketing copy creation. We conduct explicit feature engineering within the prompts of LLM. Additionally, we modify the crossover operator in Genetic Algorithm (GA), integrating it into the GCOF to enable automatic feature engineering. This integration facilitates a self-iterative refinement of the marketing copy. Compared to human curated copy, Online results indicate that copy produced by our framework achieves an average increase in click-through rate (CTR) of over 50%.
@article{arxiv.2402.13667,
title = {GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model},
author = {Jianghui Zhou and Ya Gao and Jie Liu and Xuemin Zhao and Zhaohua Yang and Yue Wu and Lirong Shi},
journal= {arXiv preprint arXiv:2402.13667},
year = {2024}
}