Assessment and evaluation have long been critical challenges in artificial intelligence (AI)
and natural language processing (NLP). However, traditional methods, whether matching …

The security concerns surrounding Large Language Models (LLMs) have been extensively
explored, yet the safety of Multimodal Large Language Models (MLLMs) remains …

Alignment tuning has become the de facto standard practice for enabling base large
language models (LLMs) to serve as open-domain AI assistants. The alignment tuning …

Most traditional AI safety research has approached AI models as machines and centered on
algorithm-focused attacks developed by security experts. As large language models (LLMs) …

While significant attention has been dedicated to exploiting weaknesses in LLMs through
jailbreaking attacks, there remains a paucity of effort in defending against these attacks. We …

Large language models (LLMs) are popular for high-quality text generation but can produce
harmful content, even when aligned with human values through reinforcement learning …

AI safety training and red-teaming of large language models (LLMs) are measures to
mitigate the generation of unsafe content. Our work exposes the inherent cross-lingual …

Warning: This paper contains examples of harmful language, and reader discretion is
recommended. The increasing open release of powerful large language models (LLMs) has …

Automated red teaming holds substantial promise for uncovering and mitigating the risks
associated with the malicious use of large language models (LLMs), yet the field lacks a …

Large Language Models (LLMs) have performed exceptionally in various text-generative
tasks, including question answering, translation, code completion, etc. However, the over …