This week’s MCQLL meeting Thursday, March 18, 1:30-2:30pm, will feature a talk from Ben LeBrun. Talk abstract is below.
If you would like to attend the talk but have not yet signed up for the MCQLL meetings this semester, please send an email to email@example.com.
Abstract: The use of pre-trained Transformer language models (TLMs) has led to significant advances in the field of natural language processing. This success has typically been measured by quantifying model performance on down-stream tasks, or through their ability to predict words in large samples of text. However, these benchmarks are biased in favour of frequent natural language constructions, measuring performance on common, recurring patterns in the data. The behaviour of TLMs on the large set of complex and infrequent linguistic constructions is in comparison understudied. In this talk, I will present preliminary results exploring GPT2’s ability to reproduce this long-tail of syntactic constructions, and how this ability is modulated by fine-tuning.