AdaPrompt: Adaptive Model Training for Prompt-based NLP
This work addresses gaps in prompt-based learning for NLP tasks, offering incremental improvements in zero-shot and few-shot performance.
The paper tackles the problem of insufficient prompt information and task-specific data representation in prompt-based NLP by proposing AdaPrompt, which adaptively retrieves external data for continual pretraining and uses Natural Language Inference models for adaptive verbalizers. Experimental results show improvements in few-shot settings and up to 26.35% relative error reduction in zero-shot settings compared to standard methods.
Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in 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 pretraining. Second, task-specific data are not necessarily well represented during pretraining. 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.