# LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Prompts

arXiv: https://arxiv.org/abs/2308.05935 · HTML: https://mlsanity.com/paper/2308.05935/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [A Large-Scale Chinese Short-Text Conversation Dataset](https://mlsanity.com/paper/2008.03946/) (2020)
- [CPM-2: Large-scale Cost-effective Pre-trained Language Models](https://mlsanity.com/paper/2106.10715/) (2021)
- [PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation](https://mlsanity.com/paper/2109.09519/) (2021)
- [EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training](https://mlsanity.com/paper/2203.09313/) (2022)
- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine](https://mlsanity.com/paper/2203.10232/) (2022)
- [Real-World Deployment and Evaluation of Kwame for Science, An AI Teaching Assistant for Science Education in West Africa](https://mlsanity.com/paper/2302.10786/) (2023)
- [Agent Smith: Teaching Question Answering to Jill Watson](https://mlsanity.com/paper/2112.13677/) (2021)

## Built on by (most cited first, 9 of 9)

- [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024)
- [Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions](https://mlsanity.com/paper/2408.15787/) (2024)
- [History, Development, and Principles of Large Language Models-An Introductory Survey](https://mlsanity.com/paper/2402.06853/) (2024)
- [From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents](https://mlsanity.com/paper/2409.03512/) (2024)
- [Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination](https://mlsanity.com/paper/2409.07372/) (2024)
- [Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language Models](https://mlsanity.com/paper/2406.11682/) (2024)
- [OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning](https://mlsanity.com/paper/2509.14803/) (2025)
- [MAIC-UI: Making Interactive Courseware with Generative UI](https://mlsanity.com/paper/2604.25806/) (2026)
- [CogEvol: Towards Efficient and Reliable Learning Environment Generation](https://mlsanity.com/paper/2608.30968/) (2026)

## Abstract (arXiv)

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.