Towards Unified Prompt Tuning for Few-shot Text Classification (2022)

Abstract: Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few-shot learning performance on downstream tasks.It would be desirable if the models can acquire some prompting knowledge before adapting to specific NLP tasks. We present the Unified Prompt Tuning (UPT) framework, leading to better few-shot text classification for BERT-style models by explicitly capturing prompting semantics from non-target NLP datasets. In UPT, a novel paradigm Prompt-Options-Verbalizer is proposed for joint prompt learning across different NLP tasks, forcing PLMs to capture task-invariant prompting knowledge. We further design a self-supervised task named Knowledge-enhanced Selective Masked Language Modeling to improve the PLM’s generalization abilities for accurate adaptation to previously unseen tasks. After multi-task learning across multiple tasks, the PLM can be better prompt-tuned towards any dissimilar target tasks in low-resourced settings. Experiments over a variety of NLP tasks show that UPT consistently outperforms state-of-the-arts for prompt-based fine-tuning.

## introduction the emergence of pre-trained language models (plms) has boosted the performance of a variety of nlp tasks @xcite . however, during fine-tuning, plms can perform poorly with few training samples due to model over-fitting @xcite . to alleviate this problem for low-resourced scenarios, natural language prompts have been applied to enable few-shot or zero-shot learning with plms @xcite . to make prompts more flexible and task-adaptive, prompt tuning freezes the plm backbone and adjusts the representations of prompts @xcite . this type of method is especially suitable for ultra-large plms that are difficult to tune. for bert-style plms, prompt-based fine-tuning has been proposed, transforming text classification tasks into cloze-style problems @xcite . to specify, task-specific discrete templates with masked language tokens are added to input texts. the result tokens of the masked positions predicted by the masked language modeling (mlm) head are used for class label prediction foot_0 . therefore, the pre-trained knowledge acquired by plms can be better utilized by "re-using" the mlm training objective. witnessing the successful usage of prompts for few-shot learning, various followingup works have been conducted, such as continuous prompt encoding @xcite , knowledgeable prompt learning @xcite , and prompt generation @xcite . recently, a few works @xcite @xcite focus on multi-it is a wonderful movie. ## upt: the proposed framework we start with a brief overview of the upt framework, followed by its detailed techniques. ## conclusion and future work in this paper, we present the unified prompt tuning framework (upt) that enables better few-shot text classification for bert-style models by explicitly capturing prompting semantics from non-target datasets. we introduce a novel pov paradigm to unify the task format, and then extend the unified prompting to the self-supervised learning with the knowledge-enhanced selective mlm task. experiments show that upt consistently outperforms state-of-the-arts for prompt-based fine-tuning over multiple text classification scenarios. as for future work, we seek to extend upt to other tasks such as named entity recognition, text generation, and machine translation. in addition, we will explore continuous prompt-tuning for upt.