11-697: Introduction to Question Answering with Large Language Models

Instructors: Eric Nyberg and Teruko Mitamura

Course Description: This course is designed to be accessible to Masters and advanced undergraduate students who seek the basic skills necessary to implement practical Question Answering (QA) applications using Large Language Models (LLMs) in specific information domains. The syllabus includes learning materials on the core concepts of QA and LLMs, and how they are applied in closed commercial systems (e.g. ChatGPT) as well as open systems (e.g. Llama, T5). Students complete a set of hands-on exercises in Python that develop skills in applying LLMs for various open-source QA datasets. The course is also a prerequisite for 11-797 Question Answering (an advanced project-oriented course).

Prerequisite Knowledge: A course in Statistics and Probability and at least intermediate Python programming skills

Course Goals: Students acquire basic knowledge of QA approaches and tasks, including Data Analysis, Solution Design, Metrics, Evaluation and Error Analysis.


Outline of Learning Materials:

  1. Foundations (Course Prerequisites, Definitions, Concepts, etc.)
  2. A First Example: LLMs for QA (e.g. ChatGPT)
  3. History: a survey of tasks, domains, methods
  4. Modern Methods: neural models, LLMs; neural architectures
  5. Elements of the QA Task
  6. Task Curation & Evaluation
  7. Neural Nets for QA
  8. Multi-Hop QA
  9. Conversational QA
  10. Multimodal QA
  11. Generative LLMs
  12. Wrap-Up