4 papers
The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models
Yunze Xiao, Vivienne J. Zhang, Chenghao Yang +3
Applications based on large language models (LLMs), such as multi-agent simulations, require population diversity among agents. We identify a pervasive failure mode we term \emph{P…
Say Something Else: Rethinking Contextual Privacy as Information Sufficiency
Yunze Xiao, Wenkai Li, Xiaoyuan Wu +3
LLM agents increasingly draft messages on behalf of users, yet users routinely overshare sensitive information and disagree on what counts as private. Existing systems support only…
PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing
Anthony Hughes, Vasisht Duddu, N. Asokan +2
Language models (LMs) may memorize personally identifiable information (PII) from training data, enabling adversaries to extract it during inference. Existing defense mechanisms su…
How Private are Language Models in Abstractive Summarization?
Anthony Hughes, Ning Ma, Nikolaos Aletras
In sensitive domains such as medical and legal, protecting sensitive information is critical, with protective laws strictly prohibiting the disclosure of personal data. This poses…