Resources
You will be able to download the external materials and exam questions here.
Exam Questions
Textbook
- Introduction to Large Language Models, Tanmoy Chakraborty
Reference Books
- Speech and Language Processing, Dan Jurafsky and James H. Martin
- Foundations of Statistical Natural Language Processing, Chris Manning and Hinrich Schütze
- Natural Language Processing, Jacob Eisenstein
- A Primer on Neural Network Models for Natural Language Processing, Yoav Goldberg
Research Papers (As Recommended in Class)
Journals
- Computational Linguistics, TACL, JMLR, TMLR, etc.
Conferences
- ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, AAAI, WWW, KDD, SIGIR, etc.
Related Courses (Non-exhaustive)
- CMU CS 11-711: Advanced NLP, Graham Neubig
- UMass Amherst CS 685: Advanced NLP, Mohit Iyyer
- Stanford CS224N: NLP with Deep Learning, Chris Manning
- Princeton COS 597G: Understanding Large Language Models, Danqi Chen
- UT Austin CS388/AI388/DSC395T: Natural Language Processing, Greg Durrett
- Stanford CS324: Large Language Models
- CMSC 473/673: Natural Language Processing at UMBC, Lara Martin
- CMU CS 11-830: Computational Ethics for NLP
- JHU CS 601.471/671 NLP: Self-supervised Models, Daniel Khashabi
- WING.NUS Large Language Models