SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains

• URL: http://arxiv.org/abs/2302.06868v1
• Date: Tue, 14 Feb 2023 07:14:08 GMT
• Title: SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
• Authors: Koustava Goswami, Lukas Lange, Jun Araki, Heike Adel
• Abstract summary: SwitchPrompt is a novel and lightweight prompting methodology for adaptation of language models trained on datasets from the general domain to diverse low-resource domains. 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.
• Score: 14.096170976149521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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