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GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model (2402.13667v1)

Published 21 Feb 2024 in cs.CL

Abstract: LLMs(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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Summary

  • The paper introduces GCOF, a novel framework that integrates explicit feature engineering in LLM prompts with a modified genetic algorithm for optimized marketing copy.
  • It leverages keywords as genes and a GPT-4 based reward model to iteratively refine copy for improved efficiency and engagement.
  • Empirical results demonstrate that GCOF enhances copy performance, achieving over a 50% increase in click-through rates compared to traditional methods.

Enhancing Marketing Copy Creation through Genetic Copy Optimization Framework (GCOF)

Introduction

The ever-evolving landscape of marketing demands innovation and efficiency in generating content that not only captures the audience's attention but also drives engagement and conversion. Traditional methods of creating marketing copy, relying heavily on human input and iterative feedback, are often time-consuming and may not always yield optimal results. Addressing this challenge, the paper introduces the Genetic Copy Optimization Framework (GCOF), a novel approach designed to automate and optimize the generation of marketing copy leveraging LLMs and an adapted Genetic Algorithm (GA).

The Core of GCOF

GCOF fundamentally transforms the process of marketing copy creation by conducting explicit feature engineering within the prompts used for LLMs, alongside a strategic modification of the GA crossover operator. This allows for a self-iterative refinement process that significantly enhances the efficiency and engagement of the resultant marketing copy. Key aspects of GCOF include:

  • Explicit Feature Engineering: By incorporating feature engineering directly into LLM prompts, GCOF utilizes operational team insights and domain-specific attributes to craft compelling marketing messages.
  • Modified Genetic Algorithm: The incorporation of an adapted crossover operator from the Differential Evolution algorithm into GA enables an automatic feature selection process, contributing to the generation of high-quality marketing copy.
  • Performance Enhancement: Online results demonstrate that marketing copy produced through GCOF achieves a substantial improvement in click-through rates (CTR), evidencing an average increase of over 50% compared to human-curated copies.

Methodological Insights

GCOF's methodology is underpinned by the seamless integration of LLM-based copy generation and GA's optimization capabilities:

  • GA in Automatic Feature Engineering: Utilizing keywords as genes, GCOF's GA component iteratively selects and optimizes these keywords to refine copy generation continually.
  • Reward Model Integration: A novel reward model, leveraging GPT-4, evaluates the generated copy's potential CTR, shaping the iterative optimization process within GCOF.
  • Empirical Validation: The methodology's effectiveness is underscored by comprehensive experiments, highlighting GCOF’s ability to surpass traditional copywriting approaches across several marketing campaigns.

Practical Implications and Future Directions

The introduction of GCOF signifies a pivotal advancement in automating and optimizing marketing copy generation. Its ability to integrate explicit feature engineering and adapt genetic algorithms within the context of LLMs paves the way for creating highly engaging and conversion-driven copy at scale.

Practically, GCOF has demonstrated its value in real-world applications, yielding significant improvements in marketing outcomes within the JD Finance App ecosystem. Such achievements underscore GCOF's potential to transform marketing operations across various domains, offering a scalable and efficient solution to drive business growth through enhanced engagement and conversion.

Looking ahead, the continuous refinement of the reward model and the exploration of Reinforcement Learning from Human Feedback (RLHF) present promising avenues to further enhance GCOF's efficacy. As the landscape of digital marketing evolves, such advancements will be crucial in maintaining the relevance and impact of automated marketing copy generation.

Concluding Remarks

In conclusion, the Genetic Copy Optimization Framework (GCOF) represents a significant step forward in the automation and optimization of marketing copy generation. By harnessing the power of LLMs and genetic algorithms, GCOF not only increases efficiency but also elevates the quality and engagement of marketing content. As we look to the future, the ongoing development and refinement of GCOF and related technologies hold the promise of revolutionizing digital marketing strategies, driving innovation, and enhancing business outcomes in the ever-competitive marketing arena.

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