Natural language reflects our private lives and identities, making its privacy concerns as
broad as those of real life. Language models lack the ability to understand the context and …

Large language models (LLMs) are excellent in-context learners. However, the sensitivity of
data contained in prompts raises privacy concerns. Our work first shows that these concerns …

Although large language models (LLMs) are widely deployed, the data used to train them is
rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but …

The wide adoption and application of Masked language models~(MLMs) on sensitive data
(from legal to medical) necessitates a thorough quantitative investigation into their privacy …

Large language models (LLMs) are excellent few-shot learners. They can perform a wide
variety of tasks purely based on natural language prompts provided to them. These prompts …

Large Language Models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

Rapid advancements in language models (LMs) have led to their adoption across many
sectors. Alongside the potential benefits, such models present a range of risks, including …

Privacy attacks on Machine Learning (ML) models often focus on inferring the existence of
particular data points in the training data. However, what the adversary really wants to know …

Neural Code Completion Tools (NCCTs) have reshaped the field of software development,
which accurately suggest contextually-relevant code snippets benefiting from language …

With large language models (LLMs) poised to become embedded in our daily lives,
questions are starting to be raised about the dataset (s) they learned from. These questions …