A da P rompt: Adaptive Model Training for Prompt-based NLP (2022)

Abstract: Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in the community.The main idea is to bridge the gap between NLP downstream tasks and language modeling (LM), by mapping these tasks into natural language prompts, which are then filled by pre-trained language models (PLMs).However, for prompt learning, there are still two salient gaps between NLP tasks and pretraining.First, prompt information is not necessarily sufficiently present during LM pre-training. Second, task-specific data are not necessarily well represented during pre-training. We address these two issues by proposing AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs by making use of both task and prompt characteristics. In addition, we make use of knowledge in Natural Language Inference models for deriving adaptive verbalizers.Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In addition, in zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.

## introduction prompt-based methods @xcite @xcite have received increasing attention in natural language processing (nlp) recently. the main idea is to make the most use of pretrained language models (plms) by adapting an nlp task into a natural language prompt, which can then be filled by plms. take sentiment classification @xcite for example. given the sentence "i love the movie.", the standard task is to make a binary classification on its sentiment polarity (i.e., positive or negative). ## method our method is based on prompt-based text classification methods (section 3.1). the overall procedure of adaprompt is shown in figure 2 , which can be divided into two parts: plm adaptation (section 3.2) and verbalizer adaptation (section 3.4). in section 3.3, we introduce a method that adapts both plms and verbalizers in an iterative way for continual improvements. ## conclusion we investigated adaprompt, a zero-shot promptbased method for nlp that makes use of test input data and prompts for adaptive continual pretraining and verbalizer selection. results on five classification datasets show that adaprompt improves over a standard prompt method by large margins. in particular, retrieving relevant data for continual pretraining of a language model can serve to warm-up the model for both domain adaptation and prompt-filling tasks. in addition, an nli model allows effective selection of filled tokens to achieve improved performance.