Overview

The goal of the course project is to give students hands-on experience in a research area of their choice, related to the topics covered in class. Teams of 2 or 3 students are recommended. Solo projects are allowed with a justification (e.g., if the project is part of your ongoing research).

The following types of projects are welcome and encouraged:

  • Paper reproduction: Reproduce any paper discussed in class or included in the recommended readings, or related work from ML/NLP conferences. Your project should:
    • Include a subset of the original experiments, along with meaningful extensions (e.g., new datasets, base models, or ablations).
    • Provide novel insights or results beyond the original paper.
    • Meta-reproducibility studies across related papers are also encouraged.
  • Discussion-inspired projects: Develop ideas inspired by in-class discussion.
  • Ongoing research: You may build on your current research (including work with collaborators outside the class), as long as it clearly relates to the course topic and is noted in all deliverables. If unsure about relevance, consult the instructor.

Please keep in mind the compute resources available to you. The instructor can’t provide compute resources for the project.

Milestones and Deliverables

There are three main deliverables: project lighting talk, the project presentation, the final paper along with the code base.

Milestone 1: Project matching survey (Deadline: 09/16)

You are encouraged to build your team, feel free to use the slack channel to post your interest/ideas and connect with your classmates there.

Milestone 2: Project Lighting Talk (10/7)

8 minutes presentation followed by a 2 minitues discussion. The presentation will cover the motivation, key ideas, the concrete plan and the expected outcome of the project

Milestone 3: Project presentation (11/18, 11/20 and 11/25)

15-20 min total per team (this can change depending on the number of teams we have). ~15 min for the talk, split between all team members. ~5 min for QA. The goal of the presentation is to convey the important high-level ideas and takeaways of your project, rather than all the details. All group members should participate in the presentation. You can split it any way that you see fit, as long as each person presents a significant chunk of it. Demos are strongly encouraged where possible!

Milestone 4: Final report and code repo (Deadline: 12/11)

  • Report:Use the unmodified COLM template. Find the latex template here. Up to 8 pages, not including references. You are welcome to include an Appendix with no page limit, but the evaluation will primarily be based on the main paper. The final paper should be comparable in quality to a conference or workshop submission. If you’re interested in submitting your work for real, feel free to reach out to the instructor for help.<>
  • Code: a link to a github repository containing your code and make it accessible to the instructor if it is private. If your repository is not visible to the instructor, your final submission will not be considered complete. We use this repository to check contributions of all team members.