2 citations · 3 across the 8 of their papers we have counts for
8 papers
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
Zhenhua Liu, Tong Zhu, Chuanyuan Tan +1
Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural language. However, these models can inadvertently memorize private information,…
Probing Language Models for Pre-training Data Detection
Zhenhua Liu, Tong Zhu, Chuanyuan Tan +3
Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchm…
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space
Jianxiang Xiang, Zhenhua Liu, Haodong Liu +3
In real-life conversations, the content is diverse, and there exists the one-to-many problem that requires diverse generation. Previous studies attempted to introduce discrete or G…
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation
Zhenhua Liu, Tong Zhu, Jianxiang Xiang +1
Data augmentation (DA) is crucial to mitigate model training instability and over-fitting problems in low-resource open-domain dialogue generation. However, traditional DA methods…
Training DNN Models over Heterogeneous Clusters with Optimal Performance
Chengyi Nie, Jessica Maghakian, Zhenhua Liu
Adjusting batch sizes and adaptively tuning other hyperparameters can significantly speed up deep neural network (DNN) training. Despite the ubiquity of heterogeneous clusters, exi…
Homologically area-minimizing surfaces mod have at worst codimension 2 singular sets asymptotically
Zhenhua Liu
De Lellis and coauthors have proved a sharp regularity theorem for area-minimizing currents in finite coefficient homology. They prove that area-minimizing mod currents are smo…