In response to rising concerns surrounding the safety, security, and trustworthiness of
Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red …

The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …

Large Language Models (LLMs) have transformed artificial intelligence by advancing
natural language understanding and generation, enabling applications across fields beyond …

Reinforcement Learning from Human Feedback (RLHF) is a vital strategy for enhancing
model safety in language models. However, annotating preference data for RLHF is a …

Large Language Models (LLMs) demonstrate remarkable capabilities across diverse
applications. However, concerns regarding their security, particularly the vulnerability to …

Making moral judgments is an essential step toward developing ethical AI systems.
Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large …

The impressive performance of Large Language Models (LLMs) has consistently surpassed
numerous human-designed benchmarks, presenting new challenges in assessing the …

Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard
and generate harmful content. Existing jailbreaking defense methods have failed to address …

Large language models (LLMs) are trained on a deluge of text data with limited quality
control. As a result, LLMs can exhibit unintended or even harmful behaviours, such as …

The emergence of Large Language Models (LLMs) is currently creating a major paradigm
shift in societies and businesses in the way digital technologies are used. While the …