Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable
Diffusion and GPT-4, yet their design does not receive the same research attention as model …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3 (. 5) and
GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Instruction tuning is an effective technique to align large language models (LLMs) with
human intent. In this work, we investigate how an adversary can exploit instruction tuning by …

Every major technical invention resurfaces the dual-use dilemma—the new technology has
the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

As AI-generated text increasingly resembles human-written content, the ability to detect
machine-generated text becomes crucial. To address this challenge, we present …

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 …