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

Large Language Models (LLMs) have shown extraordinary capabilities in understanding
and generating text that closely mirrors human communication. However, a primary …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Recent advancements in large language models (LLMs) have significantly boosted the rise
of Role-Playing Language Agents (RPLAs), ie, specialized AI systems designed to simulate …

Despite extensive diagnostics and debugging by developers, AI systems sometimes exhibit
harmful unintended behaviors. Finding and fixing these is challenging because the attack …

Large Language Models (LLMs) have shown impressive performance in natural language
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …

Generative models are rapidly gaining popularity and being integrated into everyday
applications, raising concerns over their safe use as various vulnerabilities are exposed. In …

Large language models (LLMs) have significantly transformed the landscape of Natural
Language Processing (NLP). Their impact extends across a diverse spectrum of tasks …

Large language models (LLMs) possess immense capabilities but are susceptible to
malicious exploitation. To mitigate the risk, safety alignment is employed to align LLMs with …

Large Language Models (LLMs) have highlighted the necessity of effective unlearning
mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims …