EECS 224 - Large Language Models
The field of natural language processing (NLP) has been transformed by massive pre-trained language models. They form the basis of all state-of-the-art systems across a wide range of tasks and have shown an impressive ability to generate fluent text and perform few-shot learning. At the same time, these models are hard to understand and give rise to new ethical and scalability challenges. In this course, students will learn the fundamentals about the modeling, theory, ethics, and systems aspects of large language models, as well as gain hands-on experience working with them.
The Goal of This Course
Offering useful, fundemental, detailed LLM knowledge to students, which are useful in research but not accessible in most trending papers.
Staff
Coursework
Your grade is based on two activities:
- In-Course Question Answering (50%)
- Final Projects (50%)
In-Course Question Answering
In each class, you will be asked some questions. Every student can only answer every question for one time. The student who correctly answer the question for the first time will be granted 1 credit. The final scores will be calculated based on the accumulated credits in the whole semester.
Final Projects
Every student should complete a final project related to LLMs and present it in the final classes.