Syllabus for COMP560: Independent Research: Remembering and forgetting in micro-LLMs
Fall 2026
Dickinson College
Instructor: John MacCormick
Learning goals
Students will:
- be able to perform experiments on large language models;
- be able to document results of research experiments;
- be able to participate in team-based research;
- gain an appreciation of current research literature for large language models.
Fairness
Everyone in the course belongs equally to our research project community. The instructor aims to create an atmosphere where everyone feels a sense of belonging and feels free to ask questions.
Teaching methods
- Independent research based on guidance from instructor and other students.
- Regular meetings of the full research team and smaller subteams as needed.
When and where
- A full team meeting will take place every Wednesday evening, 7pm-8pm. Attendance is expected; see the attendance policy.
- There are no formal classroom sessions.
- Office hours: see the instructor’s office hour webpage.
Books and resources
There is no textbook. Resources will be provided via the project website and a Microsoft Teams site.
Assessment and grading
The final grade will be assessed via four equally weighted marking periods (MP1-MP4) and a cumulative achievement score (CA):
- MP1 ends at 11:59pm, Tue Sep 2
- MP2 ends at 11:59pm, Tue Oct 13
- MP3 ends at 11:59pm, Tue Nov 10
- MP4 ends at 11:59pm, Sat Dec 19
- CA is assessed at the end of the exam period based on all outputs during the semester.
Each of the above items is worth 20% of the final grade.
Minimum time expectation (MTE)
Grades for the course are based largely on effort. Effort is measured in various ways, but one important indicator is the amount of time dedicated to the project. For this reason we define the minimum time expectation (MTE) for the course as
- MTE = 8 hours per week for the full-credit version of the course;
- MTE = 4 hours per week for the half-credit version of the course.
Rubric for each marking period (MP1-MP4)
For each marking period, a student will receive a score according to the following rubric. The notions of activity log, deliverable, research achievement, and required activities are defined in more detail later.
| Score | Criteria |
|---|---|
| 90-100 | Activity log and deliverables provide convincing evidence of meeting the MTE and include providing help to other students; contributions are of very high quality and at least some contributions represent research achievement; meeting attendance and required activities adhere to policy |
| 80-89 | Activity log and deliverables provide convincing evidence of meeting the MTE; contributions are of good quality but need not represent research achievement; meeting attendance and required activities adhere to policy |
| 70-79 | Activity log and deliverables provide evidence of substantial effort (a significant fraction of the MTE); contributions meet minimal expectations for quality; meeting attendance and required activities adhere to policy |
| 60-69 | Activity log and deliverables provide evidence of substantial effort (a significant fraction of the MTE); contributions do not meet minimal expectations for quality |
| <60 | Activity log and deliverables do not provide evidence of substantial effort |
An informal summary of these criteria is as follows. You can get a score in the B range purely by devoting a reasonable amount of effort to the project. In this range, effort is far more important than achievement. To get into the A range, you will need to help other students with the project and produce at least some outputs that go beyond pre-existing results. Strikingly original research is not required, just something that demonstrates an ability to try something new and analyze the results – something that can be considered an achievement.
Rubric for cumulative achievement (CA)
For the cumulative achievement (CA) score, deliverables must include a final report, a final poster, or both. The course web pages provide instructions for preparing these deliverables. The final report and/or poster are due at the end of the exam period.
Activity log
An activity log is a private Microsoft Teams channel in which a student keeps a log of all activity for the course, preferably updated in real time several times per week. A public activity log post is a post in the public channel that summarizes the student’s activity for the week. Activity logs are discussed in more detail on the separate activity log page.
Deliverables
A deliverable is any output produced by work on the project. Deliverables include but are not limited to:
- source code checked in to a project repository;
- experimental results checked in to a project repository;
- reports, posters, and presentations;
- documentation, discussion, analysis, and other technical documents checked in to a project repository (edits to existing documents also count);
Research achievement
A research achievement is any deliverable that goes beyond existing scientific results or techniques. Typically a research achievement demonstrates some original thinking. For example, research achievements could include: a new type of experiment; a new explanation or analysis of an existing experiment; or a code feature that adds some new scientifically meaningful functionality. A research achievement need not be strikingly original. It can closely resemble existing work, but must demonstrate some extension of pre-existing content.
Helping other students
It is an expectation that experienced students will provide substantial amounts of help to inexperienced students. Helping others is a highly valued activity and should be emphasized in a student’s activity log. To achieve an outstanding grade in the course, it will be necessary to provide substantial assistance to other students.
Required activities
Required activities include
- weekly public activity log posts (PAL1-PAL12 on the course schedule, and see also the activity log page);
- attendance at weekly lab meetings (LM1-LM12 on the course schedule, and see also the attendance policy);
- presenting a chalk talk at a weekly meeting (see the chalk talk page);
- participate in an interaction meeting with another student at least once every marking period (see the interaction meeting page).
Grade threshold
The following thresholds, or possibly more generous thresholds, will be used for final grades: 93%=A; 90%=A−; 87%=B+; 83%=B; …; 60%=D−.
Plagiarism, copying, collaborating, and AI
Use of all relevant AI tools is encouraged and expected for this research project. AI use should usually be acknowledged, although there are exceptions such as boilerplate code. Students are responsible for ensuring that all content over which they claim authorship is correct and original.
All work can be done in teams or individually. Teams can change on an ad hoc basis during the semester. As with any other scientific research, joint work should be attributed appropriately to all contributors.
The College’s standard policies on plagiarism apply, and you should be familiar with them.
Accommodations
The instructors will follow college policy on Accommodating Students with Disabilities.