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…
How Vulnerable Are Edge LLMs?
Ao Ding, Hongzong Li, Zi Liang +5
Large language models (LLMs) are increasingly deployed on edge devices under strict computation and quantization constraints, yet their security implications remain unclear. We stu…
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…