True Few-Shot Learning with Prompts—A Real-World Perspective

• Published in: Transactions of the Association for Computational Linguistics Vol. 10; pp. 716 - 731
• Main Authors: Schick, Timo, Schütze, Hinrich
• Format: Journal Article
• Language: English
• Published: One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA MIT Press 17.06.2022; MIT Press Journals, The; The MIT Press
• Subjects: Classification; Human performance; Learning; Linguistics; Machine learning; Natural language processing

Prompt-based approaches excel at few-shot learning. However, Perez et al. ( ) recently cast doubt on their performance as they had difficulty getting good results in a “true” few-shot setting in which prompts and hyperparameters cannot be tuned on a dev set. In view of this, we conduct an extensive...