Prompt-learning has been a widely-used paradigm in the field of Natural Language Processing (NLP), especially when data is limited. It follows the idea of pretraining-finetuning paradigm, where pre-trained language models (PLMs) will be adapted to various downstream tasks. Instead of adding task-specific objectives in finetuning, prompt-learning converts each original task to a specific prompting function. This process is named prompt engineering.
Their are two main approaches in prompt engineering: cloze prompts and prefix prompts. Cloze prompts are more suitable in downstream tasks using masked LMs, while prefix prompts are generally used in text generation tasks.
Generally, the input texts and labels can be obtained from the task dataset. These data will be rearranged into a cloze-style phrase, which includes two steps:
- Applying a template which consists of an input slot, an answer slot, and self-defined natural language texts
- Applying a verbalizer that maps the original labels to answers for the answer slot.
As shown in the following example, the dataset contains two input texts: premise and hypothesis. The label indicates the relationship between premise and hypothesis, and there are three possible text strings for label: entailment, contradiction and neutral.
We can design a meaningful cloze template as below
Instead of designing a prompt in real natural language, prefix prompts directly modifies the embedding space. It inserts a sequence of task-specific vectors and freezes the LM parameters. An advantage of prefix prompts is that embeddings of templates are no longer limited by parameters in pre-trained LMs.
Here is an example that compares prefix tuning and fine-tuning in the embedding space of transformers，the grey parts of the transformer are frozen during prefix tuning:
Liu, P. (2021, July 28). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting. . . arXiv.Org. https://arxiv.org/abs/2107.13586
Ding, N. (2021, November 3). OpenPrompt: An Open-source Framework for Prompt-learning. arXiv.Org. https://arxiv.org/abs/2111.01998
Schick, T. (2020, January 21). Exploiting Cloze Questions for Few Shot Text Classification and. . . arXiv.Org. https://arxiv.org/abs/2001.07676
Li, X. L. (2021, January 1). Prefix-Tuning: Optimizing Continuous Prompts for Generation. arXiv.Org. https://arxiv.org/abs/2101.00190