GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model
This work addresses the problem of creating effective marketing content for businesses, though it is incremental as it builds on existing genetic algorithms and LLM prompting techniques.
The paper tackles the challenge of generating marketing copy that meets domain-specific engagement requirements using large language models, achieving an average click-through rate increase of over 50% compared to human-curated copy.
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 $50\%$.