LittleMu: Deploying an Online Virtual Teaching Assistant via
  Heterogeneous Sources Integration and Chain of Teach Prompts
Shangqing Tu Zheyuan Zhang Jifan Yu Chunyang Li Siyu Zhang Zijun Yao Lei Hou Juanzi Li
Teaching assistants have played essential roles in the long history of
education. However, few MOOC platforms are providing human or virtual teaching
assistants to support learning for massive online students due to the
complexity of real-world online education scenarios and the lack of training
data. In this paper, we present a virtual MOOC teaching assistant, LittleMu
with minimum labeled training data, to provide question answering and chit-chat
services. Consisting of two interactive modules of heterogeneous retrieval and
language model prompting, LittleMu first integrates structural, semi- and
unstructured knowledge sources to support accurate answers for a wide range of
questions. Then, we design delicate demonstrations named "Chain of Teach"
prompts to exploit the large-scale pre-trained model to handle complex
uncollected questions. Except for question answering, we develop other
educational services such as knowledge-grounded chit-chat. We test the system's
performance via both offline evaluation and online deployment. Since May 2020,
our LittleMu system has served over 80,000 users with over 300,000 queries from
over 500 courses on XuetangX MOOC platform, which continuously contributes to a
more convenient and fair education. Our code, services, and dataset will be
available at https://github.com/THU-KEG/VTA.