InferAligner: Inference-Time Alignment for Harmlessness through
  Cross-Model Guidance
Pengyu Wang Dong Zhang Linyang Li Chenkun Tan Xinghao Wang Ke Ren Botian Jiang Xipeng Qiu
With the rapid development of large language models (LLMs), they are not only
used as general-purpose AI assistants but are also customized through further
fine-tuning to meet the requirements of different applications. A pivotal
factor in the success of current LLMs is the alignment process. Current
alignment methods, such as supervised fine-tuning (SFT) and reinforcement
learning from human feedback (RLHF), focus on training-time alignment and are
often complex and cumbersome to implement. Therefore, we develop
\textbf{InferAligner}, a novel inference-time alignment method that utilizes
cross-model guidance for harmlessness alignment. InferAligner utilizes safety
steering vectors extracted from safety-aligned model to modify the activations
of the target model when responding to harmful inputs, thereby guiding the
target model to provide harmless responses. Experimental results show that our
method can be very effectively applied to domain-specific models in finance,
medicine, and mathematics, as well as to multimodal large language models
(MLLMs) such as LLaVA. It significantly diminishes the Attack Success Rate
(ASR) of both harmful instructions and jailbreak attacks, while maintaining
almost unchanged performance in downstream tasks.