为大模型生成内容设计可灵活适配的安全防护机制
Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)
- 提出动态自适应序列框架，集成信任与安全模块
- 可有效防止大模型泄露隐私、生成虚假信息
- 适合需要合规部署的AI应用开发者参考
大语言模型（LLM）作为生成式AI的核心，正被广泛应用于各类应用中。然而，其快速发展也带来安全、隐私和伦理风险：可能泄露敏感信息、生成虚假内容，或被恶意利用。为保障生成内容的安全性与伦理性，亟需在应用层面部署防护机制。本文提出一种灵活自适应序列（Flexible Adaptive Sequencing）机制，结合信任与安全模块，可在开发与部署阶段实现对大模型的有效监管，防止滥用，确保输出内容可信、合规。该框架具备良好的可扩展性，适用于多场景下的大模型应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。