Trust-Oriented Adaptive Guardrails for Large Language Models
This work addresses the need for more personalized and secure content moderation in LLMs for users and developers, though it appears incremental by building on existing trust modeling and retrieval-augmented generation techniques.
The paper tackles the problem of existing guardrails for large language models lacking adaptability to diverse user needs, particularly in access rights, by introducing a trust-oriented adaptive guardrail mechanism that dynamically moderates content based on user trust metrics, and it demonstrates effectiveness in outperforming existing guardrails while securing sensitive information.
Guardrail, an emerging mechanism designed to ensure that large language models (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 (on `technical' 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.