Scholarlink

In today’s digital economy, where personalization has become a cornerstone of effective marketing strategies, companies face the dual challenge of increasing advertising impact while safeguarding sensitive customer information. Despite the rapid progress of large language models (LLMs), existing commercial solutions often neglect the integration of synthetic data to reduce privacy risks and enhance adaptability, leaving organizations dependent on external providers. To address this gap, our work fine-tunes open-source LLMs (LlaMa2, Mistral, and Zephyr) with synthetic datasets generated via GPT, aiming to produce customized marketing emails tailored to demographic and behavioral features. This thesis demonstrates not only the feasibility but also the competitiveness of such models by evaluating outputs with standard metrics (BLEU, ROUGE) and human-like scoring through GPT-4, showing that open-source models can approximate the performance of proprietary alternatives at significantly lower cost. The results confirm that fine-tuned LLMs with synthetic data represent a viable solution for enterprises seeking efficiency, personalization, and internal control of data.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From Prompts to Persuasion: Parameter-Efficient Adaptation of Open LLMs for Privacy-Aware Email Marketing

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Jordan Cardenas,
  • Fabian Cardenas,
  • Marcos Levano,
  • Billy Peralta

摘要

In today’s digital economy, where personalization has become a cornerstone of effective marketing strategies, companies face the dual challenge of increasing advertising impact while safeguarding sensitive customer information. Despite the rapid progress of large language models (LLMs), existing commercial solutions often neglect the integration of synthetic data to reduce privacy risks and enhance adaptability, leaving organizations dependent on external providers. To address this gap, our work fine-tunes open-source LLMs (LlaMa2, Mistral, and Zephyr) with synthetic datasets generated via GPT, aiming to produce customized marketing emails tailored to demographic and behavioral features. This thesis demonstrates not only the feasibility but also the competitiveness of such models by evaluating outputs with standard metrics (BLEU, ROUGE) and human-like scoring through GPT-4, showing that open-source models can approximate the performance of proprietary alternatives at significantly lower cost. The results confirm that fine-tuned LLMs with synthetic data represent a viable solution for enterprises seeking efficiency, personalization, and internal control of data.