Decoupled Alignment for Robust Plug-and-Play Adaptation

This innovation is practically urgent and important. LLMs have been widely adopted in various applications recently, demonstrating their ability to generate high-quality human-like texts [Team et al., 2024, Touvron et al., 2023, Ivison et al., 2023]. However, the security of these models has become a significant concern due to the potential risks of generating harmful content [Wu et al., 2024a, Yu et al., 2024, 2023a, Chao et al., 2023, Deng et al., 2023]. To align the LLMs with ethical guidelines, researchers have developed various methods to enhance their safety. For example, the Llama-2-Chat [Touvron et al., 2023] and Gemma-it [Team et al., 2024] models have been extensively fine-tuned to improve their alignment performance. However, these methods often require extensive computational resources or manual red-teaming, which can be costly and time-consuming [Team et al., 2024, OpenAI, 2024, Bai et al., 2022, Ganguli et al., 2022]. Thus, most of the LLMs finetuned from the pre-trained models by third-party developers do not undergo the alignment process [Xu et al., 2024a, Chiang et al., 2023, Ivison et al., 2023], leaving them vulnerable to generating harmful content by users with malicious intent. To combat these issues, we seek motivations from knowledge distillation technologies [Xu et al., 2024b, Hahn and Choi, 2019], where a teacher model's knowledge is transferred to a student model. Specifically, through numerical experiments Figure 3 and Figure 4, we make two key detections: MLP Alignment.