Imagine an AI assistant that not only gives helpful advice but also knows when to refuse a request, especially a harmful one. That's the goal of a new technique called Decoupled Refusal Training (DeRTa), designed to make Large Language Models (LLMs) safer. Current safety training methods often teach LLMs to refuse harmful requests only at the beginning of a response. This can be problematic because the LLM may not have enough context to understand the potential dangers. DeRTa addresses this by teaching LLMs to refuse at *any point* in a conversation, even if they initially started generating a harmful response. This is like giving the LLM a stronger moral compass, intervening before harm is done. DeRTa works by exposing the LLM to examples of harmful responses during training, making the model aware of how these responses begin and how to pivot to a safe refusal. This training includes a clever technique called "Reinforced Transition Optimization" that strengthens the LLM’s ability to switch from a potentially harmful response to a safe refusal, no matter how far down the "wrong path" the conversation has gone. Tests show DeRTa dramatically improves safety across different LLMs, like the LLaMA and Mistral models, outperforming even giants like GPT-4 in defending against various attacks. The best part? This safety boost doesn't come at the cost of helpfulness – the LLMs remain just as capable at handling normal requests. DeRTa is a promising step towards building AI systems that not only generate impressive text but also act responsibly, refusing to participate in activities that could cause harm. This approach represents a significant shift in AI safety, moving beyond simple yes/no filters to instilling a more nuanced and responsible approach to conversation. As LLMs become integrated into more aspects of our lives, ensuring they have this kind of "built-in safety mechanism" will be paramount.