Trust-Oriented Adaptive Guardrails for Large Language Models (2408.08959v3)

Abstract: Guardrail, an emerging mechanism designed to ensure that LLMs align with human values by moderating harmful or toxic responses, requires a sociotechnical approach in their design. This paper addresses a critical issue: existing guardrails lack a well-founded methodology to accommodate the diverse needs of different user groups, particularly concerning access rights. Supported by trust modeling (primarily on social' aspect) and enhanced with online in-context learning via retrieval-augmented generation (ontechnical' aspect), we introduce an adaptive guardrail mechanism, to dynamically moderate access to sensitive content based on user trust metrics. User trust metrics, defined as a novel combination of direct interaction trust and authority-verified trust, enable the system to precisely tailor the strictness of content moderation by aligning with the user's credibility and the specific context of their inquiries. Our empirical evaluation demonstrates the effectiveness of the adaptive guardrail in meeting diverse user needs, outperforming existing guardrails while securing sensitive information and precisely managing potentially hazardous content through a context-aware knowledge base. To the best of our knowledge, this work is the first to introduce trust-oriented concept into a guardrail system, offering a scalable solution that enriches the discourse on ethical deployment for next-generation LLM service.

• The paper introduces an adaptive guardrail system for large language models that dynamically adjusts access to sensitive content based on a user's composite trust score derived from interaction history and third-party verification.
• It proposes calculating a user's trust score by combining direct interaction history with authority-verified trust and maps this score to different guardrail tiers that regulate content access and richness.
• Experimental results demonstrate that this system improves the success rate for high-trust users accessing relevant information while effectively restricting hazardous content compared to static methods.

This paper introduces a novel approach to guardrails for LLMs by implementing an adaptive system that modulates access to sensitive content based on user trust. It addresses the limitations of existing static guardrails that apply uniform rules to all users, regardless of their individual needs or access rights. The proposed adaptive guardrail mechanism uses trust modeling and in-context learning to dynamically adjust content moderation based on user trust metrics.

The core idea is to tailor the strictness of content moderation to align with a user's credibility and the specific context of their inquiries. The system leverages a combination of "Direct Interaction Trust," derived from the user's historical interactions with the LLM, and "Authority-Verified Trust," established through credential verification by Trusted Third Parties (TTPs), to calculate a composite trust score. This score then determines the level of access granted to sensitive information. In-context learning is used to customize responses to sensitive queries according to the obtained trust score.

The paper details the development of a trust model specifically tailored for LLMs, the implementation of adaptive guardrails that adjust access levels based on trust scores and in-context learning, and an empirical analysis of the effectiveness of the proposed approach.

The methodology involves:

1. Trust Evaluation: Computing a composite trust score (T) based on Direct Interaction Trust (DT) and Authority-Verified Trust (AT). DT is calculated using factors like interaction history, time decay, and interaction consistency. AT is derived from feedback from TTPs, considering factors such as similarity of views, confidence level, and area relevance.
2. Contextual Adaptation: Classifying adaptive guardrails into different tiers that regulate the strictness of control mechanisms and access to hierarchical knowledge bases. Users with higher trust scores are granted access to more sensitive and confidential information. The content richness of LLM responses is dynamically adjusted based on the user's trust score and the corresponding guardrail tier.

The paper presents several experimental cases to demonstrate the effectiveness of the adaptive guardrail system:

• Adaptability and Security Analysis: The adaptive guardrail system is benchmarked against state-of-the-art methods, including the GPT series, Llama Guard, and Nvidia NeMo. The results show that the proposed system achieves a higher success rate in granting access to relevant content for high-trust users while effectively restricting access to irrelevant sensitive content.
• Contextual Implications Analysis: The interplay between user trust scores and access to information of varying sensitivity levels is explored. The system is shown to effectively limit access to extremely hazardous information to verified, highly-trustable users.
• Ablation Test Analysis: The impact of omitting key dimensions of AT (Area Relevance, Authority Indicator, Similarity Score, Confidence, and Normalized Rating) is evaluated, demonstrating the importance of each variable for balancing user access with system security.

The conclusion emphasizes that this trust-oriented method effectively secures sensitive information without compromising user engagement, thereby meeting diverse user needs. Future work will focus on more comprehensive contextual hierarchy and deeper integration with emerging AI technologies.