  Larger language models (LLMs) have taken the world by storm with their
massive multi-tasking capabilities simply by optimizing over a next-word
prediction objective. With the emergence of their properties and encoded
knowledge, the risk of LLMs producing harmful outputs increases, making them
unfit for scalable deployment for the public. In this work, we propose a new
safety evaluation benchmark RED-EVAL that carries out red-teaming. We show that
even widely deployed models are susceptible to the Chain of Utterances-based
(CoU) prompting, jailbreaking closed source LLM-based systems such as GPT-4 and
ChatGPT to unethically respond to more than 65% and 73% of harmful queries. We
also demonstrate the consistency of the RED-EVAL across 8 open-source LLMs in
generating harmful responses in more than 86% of the red-teaming attempts.
Next, we propose RED-INSTRUCT--An approach for the safety alignment of LLMs. It
constitutes two phases: 1) HARMFULQA data collection: Leveraging CoU prompting,
we collect a dataset that consists of 1.9K harmful questions covering a wide
range of topics, 9.5K safe and 7.3K harmful conversations from ChatGPT; 2)
SAFE-ALIGN: We demonstrate how the conversational dataset can be used for the
safety alignment of LLMs by minimizing the negative log-likelihood over helpful
responses and penalizing over harmful responses by gradient accent over sample
loss. Our model STARLING, a fine-tuned Vicuna-7B, is observed to be more safely
aligned when evaluated on RED-EVAL and HHH benchmarks while preserving the
utility of the baseline models (TruthfulQA, MMLU, and BBH).
