Princeton: Understanding Large Language Models
FreeAdvanced graduate course on understanding large language models
FreeFree tier
About Princeton: Understanding Large Language Models
This is a graduate course from Princeton University (Fall 2022) covering pre-trained large language models (LLMs). Topics include technical foundations such as BERT, GPT, T5, mixture-of-expert models, and retrieval-based models; emerging capabilities like knowledge, reasoning, few-shot learning, and in-context learning; fine-tuning and adaptation methods; system design; and security and ethics. Students engage in reading and presenting research papers and complete a final research project. The course is intended to prepare students for cutting-edge research in NLP.
Key Features
Covers technical foundations of pre-trained language models (BERT, GPT, T5, mixture-of-experts, retrieval-based)
Includes emerging capabilities (knowledge, reasoning, few-shot learning, in-context learning)
Discusses fine-tuning, adaptation, and system design
Addresses security and ethical challenges of LLMs
Hands-on final research project from ideation to paper writing
Emphasis on reading and presenting research papers
Pros & Cons
Pros
- Comprehensive coverage of modern LLM topics from foundations to ethics
- Includes both technical depth and practical research experience
- Lectures designed around cutting-edge papers and student-led presentations
- Taught by leading researcher Danqi Chen
Cons
- Course materials are from Fall 2022 and may not cover very recent developments
- Requires prior knowledge of machine learning and NLP (graduate-level prerequisite)
Best For
Academic research in natural language processing (NLP)Understanding state-of-the-art language models and their capabilitiesPreparing for research on large language modelsLearning fine-tuning and adaptation techniques for LLMs
FAQ
What does this course cover?
It covers pre-trained language models including BERT, GPT, T5, mixture-of-experts, retrieval-based models, emerging capabilities, fine-tuning, system design, security, and ethics.
Who is the instructor?
Danqi Chen, with teaching assistant Alexander Wettig.
What are the prerequisites?
Students are expected to have taken machine learning and NLP courses and be familiar with deep learning models such as Transformers.
How is the course structured?
Class participation (pre-lecture questions), student-led presentations (30% of grade), and a final research project.