2 citations · 2 across the 4 of their papers we have counts for
4 papers
Beyond the Stitching Assumption: A Unified Framework for Multimodal Synthetic Data Evaluation via Semantic Quantization
Yefeng Yuan, Zhan Shi, Liang Cheng +1
Multimodal synthetic datasets combine structured attributes with free text, but are often evaluated separately. Such metrics can remain high after tabular--text pairings are disrup…
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…
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…
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…