mlsanityData to 14 Oct 2026

Agent Smith: Teaching Question Answering to Jill Watson

arXiv 2112.13677 ↗First posted 22 Dec 2021cs.LG cs.AI cs.HCcited by 4

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Abstract (arXiv)

Building AI agents can be costly. Consider a question answering agent such as Jill Watson that automatically answers students' questions on the discussion forums of online classes based on their syllabi and other course materials. Training a Jill on the syllabus of a new online class can take a hundred hours or more. Machine teaching - interactive teaching of an AI agent using synthetic data sets - can reduce the training time because it combines the advantages of knowledge-based AI, machine learning using large data sets, and interactive human-in-loop training. We describe Agent Smith, an interactive machine teaching agent that reduces the time taken to train a Jill for a new online class by an order of magnitude.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 22 Dec 2021.

Builds on

8 of 17 references.

References · 8

  1. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  3. 2020Logic-Guided Data Augmentation and Regularization for Consistent Question Answeringcited by 78
  4. 2018An Overview of Machine Teachingcited by 63
  5. 2017Iterative Machine Teachingcited by 41
  6. 2019XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answeringcited by 35
  7. 2020AI-Powered Learning: Making Education Accessible, Affordable, and Achievablecited by 9
  8. 2018A Survey Investigating Usage of Virtual Personal Assistantscited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.