For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

When transferring a pretrained model to a downstream task, two popular methods are full
fine-tuning (updating all the model parameters) and linear probing (updating only the last …

Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of
data distributions when performing zero-shot inference (ie, without fine-tuning on a specific …

We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …

The Transformer architecture has become a dominant choice in many domains, such as
natural language processing and computer vision. Yet, it has not achieved competitive …

The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …

Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety
of benchmarks. However, recent works (Kumar et al., 2022; Wortsman et al., 2021) have …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …