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Current methods for few-shot fine-tuning of pretrained masked language models(PLMs) require carefully engineered prompts and verbalizers for each new taskto convert examples into a cloze-format that the PLM can score. In this work,we propose PERFECT, a simple and efficient method for few-shot fine-tuning ofPLMs without relying on any such handcrafting, which is highly effective givenas few as 32 data points. PERFECT makes two key design choices: First, we showthat manually engineered task prompts can be replaced with task-specificadapters that enable sample-efficient fine-tuning and reduce memory and storagecosts by roughly factors of 5 and 100, respectively. Second, instead of usinghandcrafted verbalizers, we learn new multi-token label embeddings duringfine-tuning, which are not tied to the model vocabulary and which allow us toavoid complex auto-regressive decoding. These embeddings are not only learnablefrom limited data but also enable nearly 100x faster training and inference.Experiments on a wide range of few-shot NLP tasks demonstrate that PERFECT,while being simple and efficient, also outperforms existing state-of-the-artfew-shot learning methods. Our code is publicly available athttps://github.com/facebookresearch/perfect.git.

PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models