GCOF: Self-iterative Text Generation for Copywriting Using Large
  Language Model
Jianghui Zhou Ya Gao Jie Liu Xuemin Zhao Zhaohua Yang Yue Wu Lirong Shi
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
$50\%$.