  Recent research has shown that large language models pretrained using
unsupervised approaches can achieve significant performance improvement on many
downstream tasks. Typically when adapting these language models to downstream
tasks, like a classification or regression task, we employ a fine-tuning
paradigm in which the sentence representation from the language model is input
to a task-specific head; the model is then fine-tuned end-to-end. However, with
the emergence of models like GPT-3, prompt-based fine-tuning has been proven to
be a successful approach for few-shot tasks. Inspired by this work, we study
discrete prompt technologies in practice. There are two issues that arise with
the standard prompt approach. First, it can overfit on the prompt template.
Second, it requires manual effort to formulate the downstream task as a
language model problem. In this paper, we propose an improvement to
prompt-based fine-tuning that addresses these two issues. We refer to our
approach as DynaMaR -- Dynamic Prompt with Mask Token Representation. Results
show that DynaMaR can achieve an average improvement of 10% in few-shot
settings and improvement of 3.7% in data-rich settings over the standard
fine-tuning approach on four e-commerce applications.
