Core Concepts
Pre-training can mitigate poor extrapolation but not dataset biases, offering complementary benefits when combined with interventions to prevent exploiting biases.
Abstract
The content explores the effectiveness of pre-training models to improve robustness in distribution shifts. It discusses how pre-training can address poor extrapolation but not dataset biases, providing insights into developing more robust models through a combination of pre-training and bias-handling interventions.
The study delves into the failure modes that pre-training can and cannot address, emphasizing the importance of understanding when pre-training is beneficial. It highlights the implications for developing robust models by combining pre-training with interventions designed to prevent exploiting biases.
Furthermore, the content examines the empirical robustness benefits of pre-training under different types of shifts, showcasing how pre-trained models exhibit effective robustness on out-of-support shifts but not on in-support shifts. It also explores the strategy of curating datasets for fine-tuning, demonstrating how a small, non-diverse de-biased dataset can lead to significantly more robust models than training from scratch on a large and diverse but biased dataset.
Overall, the content provides valuable insights into leveraging pre-training for improving model robustness in distribution shifts and emphasizes the importance of considering specific failure modes to enhance model performance.
Stats
"Models tend to suffer from distribution shifts."
"Fine-tuning a pre-trained model often significantly improves performance."
"Pre-trained models exhibit little effective robustness on in-support shifts."
"Pre-trained models have substantial effective robustness on out-of-support shifts."
Quotes
"Pre-training can help mitigate poor extrapolation but not dataset biases."
"Combining pre-training with interventions designed to handle bias yields complementary benefits."