The AI era has ushered in Large Language Models (LLM) to the technological forefront, which has been much of the talk in 2023, and is likely to remain as such for many years to come. LLMs are the AI models that are the power house behind generative AI applications such as ChatGPT. These AI models, fueled by vast amounts of data and computational prowess, have unlocked remarkable capabilities, from human-like text generation to assisting with natural language understanding (NLU) tasks. They have quickly become the foundation upon which countless applications and software services are being built, or at least being augmented with. However, as with any groundbreaking innovations, the rise of LLMs brings forth critical safety, privacy, and ethical concerns. These models are found to have a propensity to leak private information, produce false information, and can be coerced into generating content that can be used for nefarious purposes by bad actors, or even by regular users unknowingly. Implementing safeguards and guardrailing techniques is imperative for applications to ensure that the content generated by LLMs are safe, secure, and ethical. Thus, frameworks to deploy mechanisms that prevent misuse of these models via application implementations is imperative. In this study, wepropose a Flexible Adaptive Sequencing mechanism with trust and safety modules, that can be used to implement safety guardrails for the development and deployment of LLMs.

翻译：人工智能时代已将大型语言模型推向技术前沿，这已成为2023年的热议话题，并可能在今后数年内持续如此。大型语言模型是生成式人工智能应用（如ChatGPT）背后的核心驱动力。这些由海量数据和强大算力驱动的AI模型展现出从类人文本生成到辅助自然语言理解任务的卓越能力。它们已迅速成为无数应用与软件服务构建或至少增强功能的基础。然而，正如任何突破性创新技术，大型语言模型的兴起也引发了关键的安全、隐私和伦理问题。研究发现这些模型存在泄露隐私信息、生成虚假信息的倾向，并可能被恶意行为者甚至普通用户在无意中诱导生成用于不法目的的内容。实施防护措施与边界控制技术对确保大型语言模型生成内容的安全可靠与符合伦理至关重要。因此，亟需建立通过应用实现防止模型滥用的部署机制框架。本研究提出一种集成可信与安全模块的柔性自适应序列机制，可用于为大型语言模型的开发与部署实施安全防护措施。