3 papers
cs.CR2025
RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting
Zhan Shi, Yefeng Yuan, Yuhong Liu +2
The performance of modern machine learning systems depends on access to large, high-quality datasets, often sourced from user-generated content or proprietary, domain-specific corp…
cs.CL2025
Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs
Tevin Atwal, Chan Nam Tieu, Yefeng Yuan +3
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…
cs.LG2025
A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models
Yefeng Yuan, Yuhong Liu, Liang Cheng
The rapid advancements in generative AI and large language models (LLMs) have opened up new avenues for producing synthetic data, particularly in the realm of structured tabular fo…