As the use of large language models (LLMs) increases for everyday tasks,
appropriate safeguards must be in place to ensure unbiased and safe output.
Recent events highlight ethical concerns around conventionally trained LLMs,
leading to overall unsafe user experiences. This motivates the need for
responsible LLMs that are trained fairly, transparent to the public, and
regularly monitored after deployment. In this work, we introduce the
"Responsible Development of Language Models (ReDev)" framework to foster the
development of fair, safe, and robust LLMs for all users. We also present a
test suite of unique prompt types to assess LLMs on the aforementioned
elements, ensuring all generated responses are non-harmful and free from biased
content. Outputs from four state-of-the-art LLMs, OPT, GPT-3.5, GPT-4, and
LLaMA-2, are evaluated by our test suite, highlighting the importance of
considering fairness, safety, and robustness at every stage of the machine
learning pipeline, including data curation, training, and post-deployment.

使用大型语言模型（LLMs）进行日常任务时，应采取适当的措施以确保输出无偏见且安全。本研究引入了 “负责任的语言模型开发（ReDev）” 框架，以促进为所有用户开发公平、安全和稳健的 LLMs。我们还提出了一套独特提示类型的测试套件，以评估 LLMs 在公平性、安全性和稳健性等方面，确保生成的回应无害且没有偏见内容。我们通过测试套件评估了四个最先进的 LLMs：OPT、GPT-3.5、GPT-4 和 LLaMA-2，突显了在机器学习流程的每个阶段（包括数据筛选、训练和部署后）考虑公平性、安全性和稳健性的重要性。