Two recent trends in NLP---the application of deep neural networks and the use of transfer learning---have resulted in many models that achieve high performance on important tasks but whose behavior on those tasks is difficult to interpret. In this seminar, we will look at methods inspired by linguistics and cognitive science for analyzing what large neural language models have in fact learned: diagnostic/probing classifiers, adversarial test sets, and artificial languages, among others. Particular attention will be paid to probing these models' _semantic_ knowledge, which has received comparably little attention compared to their syntactic knowledge. Students will acquire relevant skills and (in small groups) design and execute a linguistically-informed analysis experiment, resulting in a report in the form of a publishable conference paper.
| Days | Time | Location |
|---|---|---|
| Wednesday | 3:30 - 5:50 PM |
SMI 309
https://washington.zoom.us/j/92378384460 |
| Role | Name | Office | Office Hours |
|---|---|---|---|
| Instructor | Shane Steinert-Threlkeld |
GUG 418D
https://washington.zoom.us/my/shanest |
Monday, 3-5 PM Pacific |
As a project-oriented, student-driven, seminar-style class, active participation---in the classroom, or in Zoom, as well as on Canvas---is expected.
All student work will be carried out in small groups. Groups are free to divide up work as they see fit, but will be required to explain the division of labor with their final project. Except under rare circumstances, every member of a group will receive the same grades.
The distribution of grades for the final grade will be:
Any questions concerning course content and logistics should be posted on the Canvas discussion board. If a more personal issue arises, you can email me personally; include "LING575" in the subject line. You can expect responses from teaching staff within 24 hours, but only during normal business hours, and excluding weekends.
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This is a list of a snapshot of some papers on interpretability / analysis of language models, organized by some keywords, reflecting my knowledge of the state of the field circa December 2019. NB: The field is large and very fast-growing, so this is by no means exhaustive and has not been updated since December 2019. To find even more literature, I recommend:
NB: the list below is an iframe, so make sure to scroll to see everything.