  Recently, the development of open-source large language models (LLMs) has
advanced rapidly. Nevertheless, due to data constraints, the capabilities of
most open-source LLMs are primarily focused on English. To address this issue,
we introduce the concept of $\textit{chat vector}$ to equip pre-trained
language models with instruction following and human value alignment via simple
model arithmetic. The chat vector is derived by subtracting the weights of a
pre-trained base model (e.g. LLaMA2) from those of its corresponding chat model
(e.g. LLaMA2-chat). By simply adding the chat vector to a continual pre-trained
model's weights, we can endow the model with chat capabilities in new languages
without the need for further training. Our empirical studies demonstrate the
superior efficacy of the chat vector from three different aspects: instruction
following, toxicity mitigation, and multi-turn dialogue. Moreover, to showcase
the adaptability of our approach, we extend our experiments to encompass
various languages, base models, and chat vectors. The results underscore the
chat vector's simplicity, effectiveness, and wide applicability, making it a
compelling solution for efficiently enabling conversational capabilities in
pre-trained language models. Our code is available at
https://github.com/aqweteddy/ChatVector.
