Adaptive Guardrails For Large Language Models via Trust Modeling and In-Context Learning

2024-08-16

arXiv.org

AI Summary

Current LLM safety guard systems lack user trust-awareness, rendering them incapable of dynamically adjusting content filtering intensity based on user identity, privilege level, and query context. To address this, we propose a user-trust-oriented dynamic content guarding mechanism—the first to incorporate explicit trust modeling into LLM safety frameworks. Our approach fuses two complementary trust signals: direct interaction behaviors and authoritative verifications, and leverages in-context learning (ICL) alongside a context-aware knowledge base to enable real-time, adaptive filtering strength calibration. The method supports multi-source trust fusion and fine-grained policy control, thereby significantly improving response precision and system practicality without compromising sensitive information protection. Empirical evaluation demonstrates robust performance across diverse user roles and scenarios. This work establishes a novel paradigm for scalable, differentiated, and ethically grounded LLM deployment.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Guardrails have become an integral part of Large language models (LLMs), by moderating harmful or toxic response in order to maintain LLMs' alignment to human expectations. However, the existing guardrail methods do not consider different needs and access rights of individual users, and treat all the users with the same rule. This study introduces an adaptive guardrail mechanism, supported by trust modeling and enhanced with in-context learning, to dynamically modulate access to sensitive content based on user trust metrics. By leveraging a combination of direct interaction trust and authority-verified trust, the system precisely tailors the strictness of content moderation to align with the user's credibility and the specific context of their inquiries. Our empirical evaluations demonstrate that the adaptive guardrail effectively meets diverse user needs, outperforming existing guardrails in practicality while securing sensitive information and precisely managing potentially hazardous content through a context-aware knowledge base. This work is the first to introduce trust-oriented concept within a guardrail system, offering a scalable solution that enriches the discourse on ethical deployment for next-generation LLMs.

Problem

• Language Models
• Access Control
• Trust Levels

Innovation

• Dynamic Trust-adjusted Guardrails
• Online Learning
• Trust Modeling