论文标题
部分可观测时空混沌系统的无模型预测
Do Prompts Solve NLP Tasks Using Natural Language?
论文作者
论文摘要
得益于大型预训练语言模型的高级改进，及时基于基于及时的微调对各种下游任务有效。尽管已经研究了许多提示方法，但在三种提示中最有效的提示仍然未知（即人为设计的提示，架构提示和空提示）。在这项工作中，我们从经验上比较了几种和完全监督的设置下的三种提示。我们的实验结果表明，架构提示通常是最有效的。此外，当训练数据的规模增长时，性能差距往往会减少。
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.
