Inducing Distribution Shift in Language Models via Word Perturbations

In this paper, the authors explore the problem of distribution shift in deep learning models, specifically in the context of natural language processing (NLP) tasks. Distribution shift occurs when the training data differs from the real-world data, leading to poor performance or incorrect predictions. The authors propose a new approach called React, which is designed to detect and handle distribution shift in NLP models.
React works by using rectified activations to transform the input data into a more linear space, allowing the model to focus on the most relevant features. The authors also introduce a new regularization term that encourages the model to produce smooth outputs, even when the input is far from the training data. This helps to reduce the impact of distribution shift and improve the model’s performance on unseen data.
The authors evaluate React using several experiments, including one where they induce distribution shift by perturing words in the prompts and responses. They show that React outperforms other state-of-the-art methods in detecting and handling distribution shift, leading to improved performance on these unseen data.
Overall, the authors demystify complex concepts like distribution shift and rectified activations by using everyday language and engaging metaphors. They provide a thorough explanation of their proposed approach while balancing simplicity and thoroughness. The summary provides a concise overview of the article without oversimplifying the complex ideas presented.