Multitask Learning Impact on Worst-Group Outcomes

Core Concepts

Multitask learning can enhance worst-group outcomes and address group-wise fairness by regularizing the joint multitask representation space.

Abstract

Multitask learning (MTL) is explored as a tool to improve worst-group accuracy and group-wise fairness. The study investigates the impact of MTL on worst-group error and proposes modifications to enhance its effectiveness. Results show that regularized MTL consistently outperforms other methods in improving both average and worst-group outcomes across various datasets. The study focuses on fine-tuning pre-trained models, demonstrating that regularized MTL can be a simple yet robust approach for improving model performance. By leveraging insights from synthetic data experiments, the study proposes a method to adapt MTL to target worst group error effectively. The results highlight the importance of pre-training in setting up regularized MTL as an effective solution for poor worst-group outcomes.

Stats

Multitasking often achieves better worst-group accuracy than Just-Train-Twice (JTT). Regularized MTL consistently outperforms JTT on both average and worst-group outcomes. Regularized MTL improves worst-group accuracy over ERM by approximately 4%. Regularized MTL outperforms JTT and Bitrate-Constrained DRO on various datasets.

Quotes

Key Insights Distilled From

Multitask Learning Can Improve Worst-Group Outcomes

by Atharva Kulk... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2312.03151.pdf

Deeper Inquiries

How does regularized multitask learning compare to other DRO methods in terms of worst-group accuracy

Regularized multitask learning has shown to be competitive with other Distributionally Robust Optimization (DRO) methods in terms of worst-group accuracy. In the context provided, regularized MTL outperformed Just Train Twice (JTT) and Bit-rate Constrained DRO (BR-DRO) on certain datasets like MNLI and Waterbirds. The approach consistently reduced the gap between Empirical Risk Minimization (ERM) and groupDRO when considering worst-group accuracy. This indicates that regularized MTL can be an effective tool for improving worst-group outcomes compared to traditional DRO methods.

What are the implications of implementing multitask learning without regularization in real-world applications

Implementing multitask learning without regularization in real-world applications can have several implications. Without regularization, the model may not effectively constrain the use of spurious features or address biases present in the data. This could lead to suboptimal performance, especially in scenarios where there are limited-to-no group annotations available during training. Additionally, without regularization, multitasking alone may not provide significant improvements in both average and worst-group performance, as observed in the study presented.

How can the findings of this study be applied to improve fairness and performance in machine learning systems

The findings of this study can be applied to improve fairness and performance in machine learning systems by incorporating regularized multitask learning techniques. By leveraging insights from synthetic data experiments and empirical explorations across various datasets, practitioners can enhance their models' robustness to worst-case group outcomes while maintaining high average performance. Regularizing joint embedding spaces through multitasking with appropriate auxiliary tasks can help mitigate biases related to demographic attributes like race or gender, thereby promoting more equitable outcomes across diverse groups within machine learning systems.