We will need you to submit all your code as a final deliverable. PLAGIARISM will be strictly penalized, see here for more details.

Project proposal (4%).

Student teams should submit a ~1-page proposal (using the *CL paper submission template) for their project by Week 5. The proposal should:

state and motivate the problem by providing a problem or task definition (preferably with example inputs and expected outputs),
situate the problem within related work (this might help you find sources of data for training a model for your task),
- Related work: publications, start by looking in the ACL anthology
- References do not count towards page limit, but please follow the correct format
state a hypothesis to be verified and how to verify it (evaluation framework), and
provide a brief description of the approach to be followed to verify the hypothesis (such as proposed models and baselines).

We highly encourage students to work towards a problem involving predictive models, hence it’s worth thinking about the five key ingredients of supervised learning: data, model, loss function, optimization algorithm and inference / evaluation.

Project progress report (8%).

Student teams should submit a ~3-page progress report (using the *CL paper submission template) for their project by the end of Week 9. This report should:

once again describe the project’s goals (it is okay if this has changed slightly since the proposal, based on the feedback),
contain all details on the dataset (your dataset should mostly be collected by this time),
contain some initial results (think of this as a motivating results), and
must outline a concrete plan of what will be done before the final report.

While the initial results might be inconclusive, you are expected to have made non-trivial progress by this point. The project proposal may be extended for this report. Please take into consideration the earlier feedback you received, and address those inline (you may highlight these in a different text color if you wish to draw the grader’s attention).

Project final presentation (10%).

Each team will prepare a 5-6 minute presentation, depending on the size of your team (5 or 6 students), followed by 2 minutes of Q/A.
Each student in the team must present an equal share of the work for 1 minute each.
Points will be deducted if the time limit is violated (i.e. a teammate speaks for longer than 1 minute, or less than 1 minute), so please practice timing your talk. We will be very strict about this.
Each project presentation should describe
- the underlying motivation of the project,
- the research questions answered in the project,
- the proposed methods, and
- their findings so far, as well as
- address audience questions.
All members of the team are expected to identify the central points of the research, and present that research to the class, as well as answer questions from the instructor, TAs and fellow students.
If you are in the audience, you could participate in asking questions - bonus points will be awarded to folks who ask insightful questions (and clearly announce their name before asking a question).
Each team will prepare slides (via Google slides) to be shared with their assigned TAs by 11:59 PM the day before the presentation. Failure to share slides on time will cause a loss of grade.

Project final report (18%).

Student teams should submit a ~6-8 page final report (again using the *CL paper submission template) detailing all aspects of their project. The report should be structured like a conference paper (similar to the papers that students read and presented in class), including

an abstract,
an introduction to their problem and method,
related work, highlighting the similarities and differences to their own work,
a description of the method used to addressed the problem,
the experiments and results, and
a discussion of the results, outlining future work possibilites. A tech report format is discouraged. Parts of the proposal and progress report may be reused for the final report. Negative results will not be penalized, but should be accompanied with detailed analysis of why the proposed method did not work as anticipated. You may include an appendix at the very end. References and the appendix do not count towards the main report page limit (i.e. can exceed 8 pages). You MUST submit all your code as a final deliverable as a zip file (points will be deducted if we do not get this in time). PLAGIARISM will be strictly penalized, see here for more details.

Example Projects

Final Class Projects from Undergraduate Special Topics: Language Models in NLP (CSCI 499; Spring 2024)

MixRx: Predicting Drug Combination Interactions with LLMs: Risha Surana, Cameron Saidock, Hugo Chacon; Report
Pseudocoder: An Analysis of Various Architectures on Python Code-To-Pseudocode Translation: Wenda Gu, Egor Cherkashin, Sarah Chen; Report
MagicRecipe: Personalized Dish Recommendation System with LLMs: Siyi He, Minhao Li, Yitian Yan; Report
WallESense: Forecast the VIX(CBOE) index from financial news: Tanvi Bhaskarwar, Venkata Meghana Achanta, Vaibhav Rungta; Report
SephoraShopper: Personalized Product Review Generation: Hilari Fan, Wonjun Lee, Seena Pourzand; Report
CuringBot: A Mental Health Chatbot with GPT-2: Rui Ji, Johnny Yang, Prithvik Gowda; Report
FAQ Generation using Language Models: Dheeraj P Anikar, Sudarshana S Rao, Rbhu Gandhi; Report
AutoRate: A Comparative Analysis of Discriminative and Generative Models for Review Ratings: Max Elgart, Rijul Raghu, Anusha Poornesh; Report
ReviewRefine: Enhancing Transparency in E-commerce and evaluating AI Summarization Techniques for Amazon Reviews: Adeline Liou, Tais Mertz; Report

Final Class Projects from Undergraduate Special Topics: Language Models in NLP (CSCI 499; Fall 2023)

Also see Stanford CS224n Projects.

Also see Stanford CS229 Machine Learning