P rompt DA : Label-guided Data Augmentation for Prompt-based Few Shot Learners (2023)

Abstract: Recent advances in large pre-trained language models (PLMs) lead to impressive gains on natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on sufficient labeled training instances, which are usually hard to obtain. Prompt-based tuning on PLMs has shown to be powerful for various downstream few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU tasks mainly focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. In addition, conventional data augmentation strategies such as synonym substitution are also widely adopted in low-resource scenarios. However, the improvements they bring to prompt-based few-shot learning have been demonstrated to be marginal. Thus, an important research question arises as follows: how to design effective data augmentation methods for prompt-based few-shot tuning? To this end, considering the label semantics are essential in prompt-based tuning, we propose a novel label-guided data augmentation framework PromptDA, which exploits the enriched label semantic information for data augmentation. Extensive experiment results on few-shot text classification tasks show that our proposed framework achieves superior performances by effectively leveraging label semantics and data augmentation for natural language understanding.

## introduction pre-trained language models (plms) have shown promising performances in various applications such as text classification @xcite , document summarization @xcite , question answering @xcite . the recent advancement of prompt-based tuning has shown a significant improvement over normal fine-tuning on various few-shot tasks @xcite . typically, the prompt-based tuning paradigm trans- (a) conventional augmentation method (synonym substitution) forms a nlu task into a masked language modeling (mlm) problem. for example, in sentiment analysis, an original sentence "nice movie to watch." can be augmented with a template "it is [mask]" as the input x. each class (e.g., positive) is represented by a label word (e.g., good) selected by a verbalizer from the vocabulary @xcite . the prediction of the class positive is based on the probability of the [mask] being filled with the token good. in addition, conventional data augmentation (da) methods such as synonym substitution are also widely applied when the training data is limited @xcite . however, it has been shown in previous works that they can only bring marginal improvements for prompt-based few-shot learning @xcite . we argue that one of the reasons could be that these da methods mainly focus on transforming the instances while not incorporating the label semantics, which have great potential to improve the performances of fewshot tasks @xcite and are essential for prompt-based few-shot learners @xcite . therefore, we focus on a new problem of designing augmentation strategies for the prompt tuning paradigm and explore fusing label semantics into augmentation for prompt-based few-shot learners. specifically, different from most prompt-based tuning methods that adopt an one-to-one verbalizer @xcite , we propose to incorporate the rich label semantic information contained in the label words derived from an one-to-multiple verbalizer into a new data augmentation paradigm. as shown in figure 1 , compared with previous data augmentation methods that mainly focus on constructing more instances, our method promptda proposes to construct instance-label pairs for augmentation, which opens a new dimension for conducting augmentation. for example, with the one-to-multiple verbalizer mapping from the class positive to a set of label words {good, great, fine}, we aim to generate a set of synthetic data points {(x, good), (x, great), (x, fine)} based on the original instance x and leverage them to enhance the performances of the prompt-based fewshot learners. furthermore, extensive experiment results in section § 5.5 also show that our proposed promptda can be regarded orthogonal to the conventional da methods (e.g., synonym substitution) to some extent. thus, promptda can complement with conventional augmentation approaches to further improve the performances. to this end, we propose a new label-guided data augmentation framework for prompt-based fewshot learners named promptda, which contains three coherent modules including label augmentation, augmented prompt-based tuning, and prediction transformation. first, we utilize a plm to automatically search for an one-to-multiple verbalizer on a specific training set and derive a set of semantically similar tokens for each class as the label words. second, in the training stage, we construct the instance-label pairs from the original data with regards to each label word for augmentation in prompt tuning. third, in the inference stage, we utilize the trained language model to predict the label by aggregating the probability scores on the derived label words. the contributions of this paper are summarized as follows: (1) we study a new problem of designing data augmentation strategies for prompt-based few-shot learners; (2) we propose a novel labelguided data augmentation framework promptda that exploits the rich label semantic information of one-to-multiple verbalizer for improving prompt tuning; (3) we conduct extensive experiments on real-world few-shot classification tasks and demonstrate the effectiveness of the proposed framework. few-shot text classification aims to build text classification model when few labeled data is available. existing works mainly follow the following categories. first, semi-supervised learning where unlabeled data, alongside usually a small amount of labeled data, is used for learning (mukherjee and awadallah, 2020; @xcite . for example, subhabrata et al. propose to jointly learn from a small set of labeled data and a large amount of unlabeled data with uncertainty using self-training @xcite . second, meta-learning frameworks such as metricbased @xcite and optimization-based approaches @xcite . third, weakly supervised learning to derive weak labels @xcite in addition to the limited clean labels to improve text classification. other approaches include transfer learning via learning to adapt transferable information from the source domain to the target domain @xcite , or leveraging auxiliary tasks to improve the target tasks @xcite . data augmentation is to construct synthetic data from an available dataset to enlarge the data size, which can help supervised training with enriched training data @xcite @xcite , or self-supervised learning for constructing samples in pretext tasks @xcite , etc. data augmentation techniques for natural language generally fall into data space and feature space @xcite . in the data space, augmentation methods transform the data in character-level, word-level, phrase-level or document-level. in the feature space, representations in the latent space are manipulated by adding noise or interpolation @xcite . however, conventional data augmentation methods bring marginal improvements under prompt tuning paradigm @xcite . it is under exploring about how to design effective data augmentation methods for prompt-based few-shot scenarios. therefore, we propose a novel labelguided data augmentation mechanism in promptbased tuning for few shot tasks. ## problem definition the goal of few-shot classification task is to learn a classifier to predict the label of unseen instances with limited labeled samples during the training. following the widely-used few-shot setting @xcite , we assume that a large pre-trained language model (e.g., bert) m can be utilized to fine-tune on a downstream task with the dataset @xmath0, y}, where x denotes the instances and y indicates the corresponding labels. for each task, the number of training instances for each class is k, which is usually small (e.g., 8 or 16). the goal is to design a prompt learning strategy that generalizes well on unseen samples in the test set d test with few labeled training data in d train . to ensure a fair parameter setting, we assume that a validation set d val is available, and |d val | = |d train |. the test set d test is the same as the full-data training setting. ## label-guided data augmentation for prompt-based tuning in this section, we detail the proposed framework promptda, which is illustrated in figure 2 . it mainly consists of three modules: (1) a label augmentation module to derive multiple label words for each class to enrich the label space; ## conclusion and future work in this paper, we study a new problem of data augmentation in prompt-based tuning for few shot learners. to leverage the label semantic information, we propose a novel label-guided data augmentation approach promptda, which can derive multiple label words and exploit the rich semantic information of the label words. we conduct extensive experiments on various datasets and demonstrate the effectiveness of promptda for few-shot learning. we also conduct detailed analysis on the effects of manual/automatic label augmentation, the size of augmentation, the size of label words, and combination with conventional da. there are several interesting directions for future work. first, we will extend promptda to multilabel few shot tasks and leverage multi-aspect label space. second, we will explore prompt-based data augmentation for token-level tasks such as fewshot name entity recognition (ner). third, we will explore prompt-based tuning to enhance the interpretability capacity for various nlp tasks.