LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models

• URL: http://arxiv.org/abs/2407.02987v1
• Date: Wed, 3 Jul 2024 10:38:40 GMT
• Title: LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models
• Authors: Hayder Elesedy, Pedro M. Esperança, Silviu Vlad Oprea, Mete Ozay,
• Abstract summary: Guardrails have emerged as an alternative to safety alignment for content moderation of large language models (LLMs) We introduce LoRA-Guard, a parameter-efficient guardrail adaptation method that relies on knowledge sharing between LLMs and guardrail models. We show that LoRA-Guard outperforms existing approaches with 100-1000x lower parameter overhead while maintaining accuracy, enabling on-device content moderation.
• Score: 15.900125475191958
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Guardrails have emerged as an alternative to safety alignment for content moderation of large language models (LLMs). Existing model-based guardrails have not been designed for resource-constrained computational portable devices, such as mobile phones, more and more of which are running LLM-based applications locally. We introduce LoRA-Guard, a parameter-efficient guardrail adaptation method that relies on knowledge sharing between LLMs and guardrail models. LoRA-Guard extracts language features from the LLMs and adapts them for the content moderation task using low-rank adapters, while a dual-path design prevents any performance degradation on the generative task. We show that LoRA-Guard outperforms existing approaches with 100-1000x lower parameter overhead while maintaining accuracy, enabling on-device content moderation.

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