Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks. Prompts can be represented by a human-engineered word sequence or by a learned continuous embedding. In this work, we investigate conditional and compositional differentiable prompting. We propose a new model, Prompt Production System (PRopS), which learns to transform task instructions or input metadata, into continuous prompts that elicit task-specific outputs from the PLM. Our model uses a modular network structure based on our neural formulation of Production Systems, which allows the model to learn discrete rules -- neural functions that learn to specialize in transforming particular prompt input patterns, making it suitable for compositional transfer learning and few-shot learning. We present extensive empirical and theoretical analysis and show that PRopS consistently surpasses other PLM adaptation techniques, and often improves upon fully fine-tuned models, on compositional generalization tasks, controllable summarization and multilingual translation, while needing fewer trainable parameters.

翻译：提示已被证明是一种有效方法，可使冻结的预训练语言模型（PLM）在下游任务中表现良好。提示可通过人工设计的词序列或学习到的连续嵌入来表示。在本工作中，我们研究了条件与组合式可微提示。我们提出了一种新模型——提示生成系统（Prompt Production System, PRopS），该模型学习将任务指令或输入元数据转换为连续提示，从而从PLM中引出特定任务的输出。我们的模型基于对生成系统的神经形式化表述，采用模块化网络结构，使其能够学习离散规则——即专门针对特定提示输入模式进行转换的神经函数，这使其适用于组合迁移学习和少样本学习。我们进行了广泛的实证与理论分析，结果表明，在组合泛化任务、可控摘要和多语言翻译中，PRopS始终超越其他PLM适配技术，且常优于完全微调模型，同时所需可训练参数更少。