  In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for
digital human applications that instruction finetunes on a wide range of
dialogue tasks in a unified internet-augmented format. Different from other
open-domain dialogue models that focus on large-scale pre-training and scaling
up model size or dialogue corpus, we aim to build a powerful and practical
dialogue system for digital human with diverse skills and good multi-task
generalization by internet-augmented instruction tuning. To this end, we first
conduct large-scale pre-training on both common document corpus and dialogue
data with curriculum learning, so as to inject various world knowledge and
dialogue abilities into ChatPLUG. Then, we collect a wide range of dialogue
tasks spanning diverse features of knowledge, personality, multi-turn memory,
and empathy, on which we further instruction tune \modelname via unified
natural language instruction templates. External knowledge from an internet
search is also used during instruction finetuning for alleviating the problem
of knowledge hallucinations. We show that \modelname outperforms
state-of-the-art Chinese dialogue systems on both automatic and human
evaluation, and demonstrates strong multi-task generalization on a variety of
text understanding and generation tasks. In addition, we deploy \modelname to
real-world applications such as Smart Speaker and Instant Message applications
with fast inference. Our models and code will be made publicly available on
ModelScope: https://modelscope.cn/models/damo/ChatPLUG-3.7B and Github:
https://github.com/X-PLUG/ChatPLUG .
