# AMPLIFY: attention-based mixup for performance improvement and label smoothing in transformer

## TL;DR

This paper introduces AMPLIFY, a new mixup method that improves model performance and reduces sensitivity to noisy data using attention mechanisms in Transformers.

## Contribution

AMPLIFY uses Transformer attention to reduce noise impact in mixup without adding parameters or high costs.

## Key findings

- AMPLIFY outperforms other mixup methods on seven benchmark text classification datasets.
- The method achieves better performance with lower computational resource consumption.
- It provides a new approach to enhance pre-trained models like BERT and RoBERTa.

## Abstract

Mixup is an effective data augmentation method that generates new augmented samples by aggregating linear combinations of different original samples. However, if there are noises or aberrant features in the original samples, mixup may propagate them to the augmented samples, leading to over-sensitivity of the model to these outliers. To solve this problem, this paper proposes a new mixup method called AMPLIFY. This method uses the attention mechanism of Transformer itself to reduce the influence of noises and aberrant values in the original samples on the prediction results, without increasing additional trainable parameters, and the computational cost is very low, thereby avoiding the problem of high resource consumption in common mixup methods such as Sentence Mixup. The experimental results show that, under a smaller computational resource cost, AMPLIFY outperforms other mixup methods in text classification tasks on seven benchmark datasets, providing new ideas and new ways to further improve the performance of pre-trained models based on the attention mechanism, such as BERT, ALBERT, RoBERTa, and GPT. Our code can be obtained at https://github.com/kiwi-lilo/AMPLIFY.