LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models
<|reference_start|>LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models: 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.<|reference_end|>
@article{elesedy2024lora-guard:,
    title={LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content
  Moderation of Large Language Models},
    author={Hayder Elesedy, Pedro M. Esperanc{c}a, Silviu Vlad Oprea, Mete Ozay},
    journal={arXiv preprint arXiv:2407.02987},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02987},
    primaryClass={cs.LG cs.AI cs.CL}
}