In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning

Xiaochuang Han

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arXiv · 2308.04275

In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning

Xiaochuang Han

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Abstract

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.

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BibTeX RIS

Xiaochuang Han. 2023-08-08. In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning. https://arxiv.org/abs/2308.04275

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