Do Prompts Solve NLP Tasks Using Natural Language?

By Sen Yang, Yunchen Zhang, Leyang Cui, Yue Zhang. 2022

6 citations recorded by the source corpus.

Thanks to the advanced improvement of large pre-trained language models, prompt-based fine-tuning is shown to be effective on a variety of downstream tasks. Though many prompting methods have been investigated, it remains unknown which type of prompts are the most effective among three types of prompts (i.e., human-designed prompts, schema prompts and null prompts). In this work, we empirically compare the three types of prompts under both few-shot and fully-supervised settings. Our experimental results show that schema prompts are the most effective in general. Besides, the performance gaps tend to diminish when the scale of training data grows large.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Prompting & Reasoning

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Citations in the corpus

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (2022, background): “Recent work has analyzed training with discrete prompts, demonstrating a boost in performance with prompting when training on various numbers of examples [71], finding that models perform similarly when trained on good and bad prompts [11], and exploring which prompts work well for few-shot and full”