Large language models (LLMs) show inherent brittleness in their safety mechanisms, as
evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This …

Large Language Models (LLMs) frequently memorize long sequences verbatim, often with
serious legal and privacy implications. Much prior work has studied such verbatim …

The pre-training and fine-tuning paradigm has demonstrated its effectiveness and has
become the standard approach for tailoring language models to various tasks. Currently …

Large Language Models (LLMs) are advancing at a remarkable pace, with myriad
applications under development. Unlike most earlier machine learning models, they are no …

In the federated setup one performs an aggregation of separate local models multiple times
during training in order to obtain a stronger global model; most often aggregation is a simple …

Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various
domains, yet understanding the intricacies of DNN decision-making and learning processes …

How does the internal computation of a machine learning model transform inputs into
predictions? In this paper, we introduce a task called component modeling that aims to …

Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical.
Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's …

Self-supervised learning (SSL) has recently received significant attention due to its ability to
train high-performance encoders purely on unlabeled data-often scraped from the internet …

The latest and most impactful advances in large models stem from their increased size.
Unfortunately, this translates into an improved memorization capacity, raising data privacy …