News
- Jan 8: Welcome all the students! Wish you enjoy our NLP course and have a splendid semester!
Information
- Lectures
- Monday, 10:30-12:15pm, Science Centre LG23 Tuesday, 16:30-17:15pm, Science Centre LG23
- Tutorials
- Tuesday, 17:30-18:15pm, Science Centre LG23
- Instructor
- Prof. Liwei WANG
- Teaching Assistant
- Yuntao Lu
- Announcement
- All updates will be sent to your emails. Please check your university emails.
- Grading
- Assignments + Paper Presentation + Final Project
Tutorial sessions would be the fastest and clearest way to discuss any problem. You are also welcomed to send emails to the instructor and the TAs.
Lectures
All related lecture materials will be posted on Blackboard → Lecture Notes.
The following schedule (tentative) are subject to minor modifications.
Lecture slide cannot be distributed outside the class.
Week | Date | Topic | Remark |
---|---|---|---|
1 | Jan 8, 9 | Intro & Basic Knowledge about NLP | |
2 | Jan 15, 16 | Neural Networks & Transformers | |
3 | Jan 22, 23 | Word and Word Vectors | |
4 | Jan 29, 30 | Language Modeling | Release of Assignment 1 Complete team forming for projects Assign selected paper to each team |
5 | Feb 5, 6 | Pretrained Large Language Models: part 1 | |
6 | Feb 19, 20 | Team Presentation on Selected Papers | The week after Lunar New Year Submission of Assignment 1 |
7 | Feb 26, 27 | Pretrained Large Language Models: part 2 | Release of Assignment 2 Release of Final Project |
8 | Mar 4, 5 | LLM applications | Submission of Assignment 2 |
9 | Mar 11, 12 | RAG + LLMs | Release of Assignment 3 |
10 | Mar 18, 19 | Language + Vision | Submission of Assignment 3 |
11 | Mar 25, 26 | Understanding and Evaluations of Large Language Models | |
12 | Apr 2 | Frontiers Topics in NLP | The day following Easter |
13 | Apr 8, 9 | Final Projects Presentation (1) | |
14 | Apr 15, 16 | Final Projects Presentation (2) | Submission of Final Project |
Assignments
There will be three assignments and one paper presentation in total.
- Three Assignments (Question Sets or Coding)
- Paper Presentation
All three assignments must be done independently by yourself.
If there is no coding involved, please submit your answers as a single PDF file. Otherwise, please compress the PDF file with related codes into a ZIP file. The PDF file should be accepted by VeriGuide before submission. All the submissions should be made via Blackboard by 11:59pm
You are encouraged to either typeset your solution in LaTeX or Word, or scan or take photos of your handwritten solution (please bundle your scans/photos into a single PDF file)
Paper presentation will be carried out by teams of four.
Each team will be randomly assigned one state-of-the-art paper from the paper pool at Week 4. Read, study, and discuss the paper thoroughly and present the paper to the class during lecture time at Week 6.
To register a team, please send an email from the team captain to TAs and cc all the team members before Week 4.
Final Project
More information about Final Project will be released later.
To maximize the project achievement, students should form teams of four in general. We recommend you using the same team as in paper presentation.
You are encouraged include in your report any illustrative, concrete examples that help you understand the papers you read.
Please submit your project report (in PDF) together with related codes and material online as a single zip file at blackboard.
Course Policy
- Academic Honesty Policy
- Late Submission Policy
- Late by less than 12 hours -- deducted by 20%
- Late by 12 to 24 hours -- deducted by 40%
- Late by 24 to 36 hours -- deducted by 60%
- Late by 36 to 48 hours -- deducted by 80%
- Submissions that are late by more than 48 hours will not be accepted.
The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on academic dishonesty.
All submissions of assignments, project, etc. should be processed by VeriGuide first.
For all the assignments and projects, late submission are subject to score deduction: