Building Guardrails for Large Language Models
Yi Dong Ronghui Mu Gaojie Jin Yi Qi Jinwei Hu Xingyu Zhao Jie Meng Wenjie Ruan Xiaowei Huang
As Large Language Models (LLMs) become more integrated into our daily lives,
it is crucial to identify and mitigate their risks, especially when the risks
can have profound impacts on human users and societies. Guardrails, which
filter the inputs or outputs of LLMs, have emerged as a core safeguarding
technology. This position paper takes a deep look at current open-source
solutions (Llama Guard, Nvidia NeMo, Guardrails AI), and discusses the
challenges and the road towards building more complete solutions. Drawing on
robust evidence from previous research, we advocate for a systematic approach
to construct guardrails for LLMs, based on comprehensive consideration of
diverse contexts across various LLMs applications. We propose employing
socio-technical methods through collaboration with a multi-disciplinary team to
pinpoint precise technical requirements, exploring advanced neural-symbolic
implementations to embrace the complexity of the requirements, and developing
verification and testing to ensure the utmost quality of the final product.