On Conditional and Compositional Language Model Differentiable Prompting
This work addresses the challenge of efficient and effective prompt-based adaptation for language models, offering improvements in compositional transfer and few-shot learning.
The paper tackles the problem of adapting pretrained language models to downstream tasks by introducing PRopS, a model that learns to generate continuous prompts from task instructions, achieving superior performance on compositional generalization, controllable summarization, and multilingual translation tasks with fewer parameters.
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.