Large Language Models (LLMs) have shown remarkable success in various tasks, yet their
safety and the risk of generating harmful content remain pressing concerns. In this paper, we …

The advent of Large Language Models (LLMs) has made a transformative impact. However,
the potential that LLMs such as ChatGPT can be exploited to generate misinformation has …

We introduce WildGuard--an open, light-weight moderation tool for LLM safety that achieves
three goals:(1) identifying malicious intent in user prompts,(2) detecting safety risks of model …

Large language models are finetuned to refuse questions about hazardous knowledge, but
these protections can often be bypassed. Unlearning methods aim at completely removing …

Social identity biases, particularly the tendency to favor one's own group (ingroup solidarity)
and derogate other groups (outgroup hostility), are deeply rooted in human psychology and …

Knowledge editing has been increasingly adopted to correct the false or outdated
knowledge in Large Language Models (LLMs). Meanwhile, one critical but under-explored …

This is the interim publication of the first International Scientific Report on the Safety of
Advanced AI. The report synthesises the scientific understanding of general-purpose AI--AI …

Chat-based language models are designed to be helpful, yet they should not comply with
every user request. While most existing work primarily focuses on refusal of" unsafe" …

Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for
their safe deployment. To better understand the underlying factors that make models safe via …

As frontier artificial intelligence (AI) systems become more capable, it becomes more
important that developers can explain why their systems are sufficiently safe. One way to do …