Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)

2023-11-01

Journal of Science & Technology

AI Summary

This work proposes a modular and configurable safety framework to address critical security and ethical risks associated with large language models (LLMs), including privacy leakage, generation of misinformation, and malicious misuse. The framework employs an adaptive sequence scheduling mechanism to dynamically integrate trustworthy components—such as content filtering, behavioral constraints, and context-aware controls—into a flexible guardrail architecture. This approach enables real-time, context-sensitive ethical and safety oversight of model outputs, effectively mitigating the generation of harmful, factually inaccurate, or privacy-sensitive content. By doing so, it significantly enhances the safety, regulatory compliance, and deployment adaptability of LLMs in real-world applications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

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, we propose 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.

Problem

• Large Language Models
• trust
• safety
• ethical concerns
• guardrails

Innovation

• Flexible Adaptive Sequencing
• Guardrails
• Large Language Models
• Trust and Safety
• Ethical AI