The University of Texas at Austin
Spring 2024
Instructor: Jessy Li
Natural language processing (NLP) and large language models (LLMs) have advanced our ability to process and interact with information at a massive scale, and shed light into our society. In this seminar, we discuss topics such as: techniques that align LLMs to human values; implicit biases of LLMs; the application of NLP to understand emotions and social discourse; the ethical implications of large-scale models; and evaluation.
Graduate standing. Prior coursework or research in Computational Linguistics/Natural Language Processing/Machine Learning/a related field in AI. Or instructor consent.
There will be no textbook for this seminar; reading material will consists of technical papers discussed in each meeting.
All extensions will be negotiated on a case-by-case basis. You must talk to the instructor before the deadline in question. If you did not obtain a permission on extension, then by default, 10 points (out of 100) will be deducted for lateness, plus an additional 5 point for every 24-hour period beyond 2 that the assignment is late.
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. Please contact the Division of Diversity and Community Engagement, Services for Students with Disabilities.
A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.