SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
This addresses the domain gap issue in NLP for low-resource settings, offering a lightweight alternative to domain-specific pre-training, though it is incremental as it builds on existing prompting techniques.
The paper tackles the problem of adapting general-domain pre-trained language models to low-resource domains for text classification, achieving up to a 10.7% accuracy increase over domain-specific models with baseline prompting methods.
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the domain gap between the pre-training data and the downstream task. In this work, we bridge this gap with a novel and lightweight prompting methodology called SwitchPrompt for the adaptation of language models trained on datasets from the general domain to diverse low-resource domains. Using domain-specific keywords with a trainable gated prompt, SwitchPrompt offers domain-oriented prompting, that is, effective guidance on the target domains for general-domain language models. Our few-shot experiments on three text classification benchmarks demonstrate the efficacy of the general-domain pre-trained language models when used with SwitchPrompt. They often even outperform their domain-specific counterparts trained with baseline state-of-the-art prompting methods by up to 10.7% performance increase in accuracy. This result indicates that SwitchPrompt effectively reduces the need for domain-specific language model pre-training.