Recent large language model (LLM) defenses have greatly improved models' ability to
refuse harmful queries, even when adversarially attacked. However, LLM defenses are …

The recent introduction of large language models (LLMs) has revolutionized the field of
robotics by enabling contextual reasoning and intuitive human-robot interaction in domains …

This study addresses a critical gap in safety tuning practices for Large Language Models
(LLMs) by identifying and tackling a refusal position bias within safety tuning data, which …

Large language models (LLMs) are vulnerable to adversarial attacks that can elicit harmful
responses. Defending against such attacks remains challenging due to the opacity of …

Neural networks are trained primarily based on their inputs and outputs, without regard for
their internal mechanisms. These neglected mechanisms determine properties that are …

Unlearning methods have the potential to improve the privacy and safety of large language
models (LLMs) by removing sensitive or harmful information post hoc. The LLM unlearning …

As AI systems become more capable, widely deployed, and increasingly autonomous in
critical areas such as cybersecurity, biological research, and healthcare, ensuring their …

Observers and practitioners of artificial intelligence (AI) have proposed an FDA-style
licensing regime for the most advanced AI models, or'frontier'models. In this paper, we …

Comprehensive evaluation of Large Language Models (LLMs) is an open research problem.
Existing evaluations rely on deterministic point estimates generated via greedy decoding …

Large Language Models (LLMs) are susceptible to security and safety threats, such as
prompt injection, prompt extraction, and harmful requests. One major cause of these …