1 citations · 1 across the 6 of their papers we have counts for
6 papers
Provable Joint Decontamination for Benchmarking Multiple Large Language Models
Zhenlong Liu, Hao Zeng, Hongxin Wei
Benchmark data contamination has become a central challenge in LLM evaluation: when evaluation examples appear in the training data of one or more audited models, reported performa…
Provable Training Data Identification for Large Language Models
Zhenlong Liu, Hao Zeng, Weiran Huang +1
Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However, existing works typically treat this t…
How does Bayesian Sampling help Membership Inference Attacks?
Zhenlong Liu, Wenyu Jiang, Feng Zhou +1
Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Existing state-of-the-art attacks typically rely on tra…
A Modulo Sampling Hardware Prototype and Reconstruction Algorithm Evaluation
Jiang Zhu, Junnan Ma, Zhenlong Liu +3
Analog-to-digital converters (ADCs) play a vital important role in any devices via manipulating analog signals in a digital manner. Given that the amplitude of the signal exceeds t…
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
Zhenlong Liu, Lei Feng, Huiping Zhuang +2
Machine learning models are susceptible to membership inference attacks (MIAs), which aim to infer whether a sample is in the training set. Existing work utilizes gradient ascent t…
Exploring Learning Complexity for Efficient Downstream Dataset Pruning
Wenyu Jiang, Zhenlong Liu, Zejian Xie +3
The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task per…