2023/10/20 by Max Vargas, Adam Tsou, Vargas, Max +5
Computer Science · #Topic Modeling #Domain Adaptation and Few-Shot Learning #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2310.13836
Sampling biases can cause distribution shifts between train and test datasets for supervised learning tasks, obscuring our ability to understand the generalization capacity of a model. This is especially important considering the wide adoption of pre-trained foundational neural networks -- whose behavior remains poorly understood -- for transfer learning (TL) tasks. We present a case study for TL on the Sentiment140 dataset and show that many pre-trained foundation models encode different representations of Sentiment140's manually curated test set M from the automatically labeled training set P, confirming that a distribution shift has occurred. We argue training on P and measuring performance on M is a biased measure of generalization. Experiments on pre-trained GPT-2 show that the features learnable from P do not improve (and in fact hamper) performance on M. Linear probes on pre-trained GPT-2's representations are robust and may even outperform overall fine-tuning, implying a fundamental importance for discerning distribution shift in train/test splits for model interpretation.