Abstract

Real-world NLP models must work well when the test distribution differs from the training distribution. While we have made great progress in natural language understanding thanks to large-scale pre-training, current models still take shortcuts and rely on spurious correlations in specific datasets. In this talk, I will discuss the role of pre-training and data in model robustness to distribution shifts. In particular, I will describe how pre-trained models avoid learning spurious correlations, when data augmentation helps and hurts, and how large language models can be leveraged to improve few-shot learning.

Please note unusual time! Georgi Karadzhov is inviting you to a scheduled Zoom meeting.

Topic: NLIP Seminar 28.01.2022 Time: Jan 28, 2022 01:00 PM London

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