Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs

• URL: http://arxiv.org/abs/2507.18055v1
• Date: Thu, 24 Jul 2025 03:12:16 GMT
• Title: Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs
• Authors: Tevin Atwal, Chan Nam Tieu, Yefeng Yuan, Zhan Shi, Yuhong Liu, Liang Cheng,
• Abstract summary: Synthetic data generated by Large Language Models (LLMs) provides cost-effective, scalable alternative to real-world data to facilitate model training.<n>We quantitatively assess the diversity (i.e., linguistic expression, sentiment, and user perspective) of synthetic datasets generated by several state-of-the-art LLMs.<n> Guided by the evaluation results, a prompt-based approach is proposed to enhance the diversity of synthetic reviews while preserving reviewer privacy.
• Score: 6.719863580831653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing use of synthetic data generated by Large Language Models (LLMs) presents both opportunities and challenges in data-driven applications. While synthetic data provides a cost-effective, scalable alternative to real-world data to facilitate model training, its diversity and privacy risks remain underexplored. Focusing on text-based synthetic data, we propose a comprehensive set of metrics to quantitatively assess the diversity (i.e., linguistic expression, sentiment, and user perspective), and privacy (i.e., re-identification risk and stylistic outliers) of synthetic datasets generated by several state-of-the-art LLMs. Experiment results reveal significant limitations in LLMs' capabilities in generating diverse and privacy-preserving synthetic data. Guided by the evaluation results, a prompt-based approach is proposed to enhance the diversity of synthetic reviews while preserving reviewer privacy.

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