113 citations · 229 across the 41 of their papers we have counts for
9 papers · 1 filter
In-Context Probing for Membership Inference in Fine-Tuned Language Models
Zhexi Lu, Hongliang Chi, Nathalie Baracaldo +3
Membership inference attacks (MIAs) pose a critical privacy threat to fine-tuned large language models (LLMs), especially when models are adapted to domain-specific tasks using sen…
When Data is the Algorithm: A Systematic Study and Curation of Preference Optimization Datasets
Aladin Djuhera, Farhan Ahmed, Swanand Ravindra Kadhe +3
Aligning large language models (LLMs) is a central objective of post-training, often achieved through reward modeling and reinforcement learning methods. Among these, direct prefer…
Evaluating the Dynamics of Membership Privacy in Deep Learning
Yuetian Chen, Zhiqi Wang, Nathalie Baracaldo +2
Membership inference attacks (MIAs) pose a critical threat to the privacy of training data in deep learning. Despite significant progress in attack methodologies, our understanding…
Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble
Zhiqi Wang, Chengyu Zhang, Yuetian Chen +3
Membership inference attacks (MIAs) pose a significant threat to the privacy of machine learning models and are widely used as tools for privacy assessment, auditing, and machine u…
Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance
Aladin Djuhera, Swanand Ravindra Kadhe, Syed Zawad +3
Recent work on large language models (LLMs) has increasingly focused on post-training and alignment with datasets curated to enhance instruction following, world knowledge, and spe…
SafeCOMM: A Study on Safety Degradation in Fine-Tuned Telecom Large Language Models
Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed +4
Fine-tuning large language models (LLMs) on telecom datasets is a common practice to adapt general-purpose models to the telecom domain. However, little attention has been paid to…