In-Context Alignment: Chat with Vanilla Language Models Before
  Fine-Tuning
Xiaochuang Han
In this note, we explore inference-time alignment through in-context
learning. We consider a vanilla pretrained language model Llama-2 before any
fine-tuning and retrieve an average of 9 demonstration alignment examples when
the model is prompted to follow chat-style instructions. Compared to direct
prompting, the in-context alignment without changing model weights leads to a
7x increase in win-rate w.r.t. the text-davinci-003 model from OpenAI, making
the vanilla language model comparable to strong baselines with alignment
fine-tuning.