4 citations · 7 across the 5 of their papers we have counts for
5 papers
Memorization Dynamics in Knowledge Distillation for Language Models
Jaydeep Borkar, Karan Chadha, Niloofar Mireshghallah +6
Knowledge Distillation (KD) is increasingly adopted to transfer capabilities from large language models to smaller ones, offering significant improvements in efficiency and utility…
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
Jaydeep Borkar, Matthew Jagielski, Katherine Lee +3
Due to the sensitive nature of personally identifiable information (PII), its owners may have the authority to control its inclusion or request its removal from large-language mode…
Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon
USVSN Sai Prashanth, Alvin Deng, Kyle O'Brien +9
Memorization in language models is typically treated as a homogenous phenomenon, neglecting the specifics of the memorized data. We instead model memorization as the effect of a se…
What can we learn from Data Leakage and Unlearning for Law?
Jaydeep Borkar
Large Language Models (LLMs) have a privacy concern because they memorize training data (including personally identifiable information (PII) like emails and phone numbers) and leak…
Simple Transparent Adversarial Examples
Jaydeep Borkar, Pin-Yu Chen
There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise tak…