Publications (36)
Privacy Auditing of Large Language Models
Ashwinee Panda, Xinyu Tang, Milad Nasr +2
Current techniques for privacy auditing of large language models (LLMs) have limited efficacy -- they rely on basic approaches to generate canaries which leads to weak membership i…
Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior
Daniel Aarao Reis Arturi, Eric Zhang, Andrew Ansah +3
Recent work has discovered that large language models can develop broadly misaligned behaviors after being fine-tuned on narrowly harmful datasets, a phenomenon known as emergent m…
SALT: Steering Activations towards Leakage-free Thinking in Chain of Thought
Shourya Batra, Pierce Tillman, Samarth Gaggar +6
As Large Language Models (LLMs) evolve into personal assistants with access to sensitive user data, they face a critical privacy challenge: while prior work has addressed output-le…
Teach LLMs to Phish: Stealing Private Information from Language Models
Ashwinee Panda, Christopher A. Choquette-Choo, Zhengming Zhang +2
When large language models are trained on private data, it can be a significant privacy risk for them to memorize and regurgitate sensitive information. In this work, we propose a…
LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation
Juzheng Zhang, Jiacheng You, Ashwinee Panda +1
Low-Rank Adaptation (LoRA) has emerged as a popular parameter-efficient fine-tuning (PEFT) method for Large Language Models (LLMs), yet it still incurs notable overhead and suffers…
Analysis of Attention in Video Diffusion Transformers
Yuxin Wen, Jim Wu, Ajay Jain +2
We conduct an in-depth analysis of attention in video diffusion transformers (VDiTs) and report a number of novel findings. We identify three key properties of attention in VDiTs:…