MarketingFM generates e-commerce ad copy with RAG and an LLM; AutoEval uses LLM-as-a-Judge plus rule checks and self-refines its prompts, with online tests showing significant clicks and impressions lifts but no significant CTR lift.
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model
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abstract
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 $50\%$.
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LLMs for Customized Marketing Content Generation and Evaluation at Scale
MarketingFM generates e-commerce ad copy with RAG and an LLM; AutoEval uses LLM-as-a-Judge plus rule checks and self-refines its prompts, with online tests showing significant clicks and impressions lifts but no significant CTR lift.