Effective Structured Prompting by Meta-Learning and Representative Verbalizer

TL;DR

MetaPrompter introduces a parameter-efficient meta-learning approach with a prompt pool and a novel verbalizer, RepVerb, to improve prompt tuning for masked language models in NLP tasks with limited data.

Contribution

It proposes MetaPrompter, combining a prompt pool and RepVerb, to enhance prompt initialization and verbalization while reducing computational costs.

Findings

MetaPrompter outperforms recent state-of-the-art methods.

RepVerb surpasses existing soft verbalizers.

Parameter efficiency is achieved by tuning only the prompt pool.

Abstract

Prompt tuning for pre-trained masked language models (MLM) has shown promising performance in natural language processing tasks with few labeled examples. It tunes a prompt for the downstream task, and a verbalizer is used to bridge the predicted token and label prediction. Due to the limited training data, prompt initialization is crucial for prompt tuning. Recently, MetaPrompting (Hou et al., 2022) uses meta-learning to learn a shared initialization for all task-specific prompts. However, a single initialization is insufficient to obtain good prompts for all tasks and samples when the tasks are complex. Moreover, MetaPrompting requires tuning the whole MLM, causing a heavy burden on computation and memory as the MLM is usually large. To address these issues, we use a prompt pool to extract more task knowledge and construct instance-dependent prompts via attention. We further propose a novel soft verbalizer (RepVerb) which constructs label embedding from feature embeddings directly. Combining meta-learning the prompt pool and RepVerb, we propose MetaPrompter for effective structured prompting. MetaPrompter is parameter-efficient as only the pool is required to be tuned. Experimental results demonstrate that MetaPrompter performs better than the recent state-of-the-arts and RepVerb outperforms existing soft verbalizers.