  Response generation is one of the critical components in task-oriented dialog
systems. Existing studies have shown that large pre-trained language models can
be adapted to this task. The typical paradigm of adapting such extremely large
language models would be by fine-tuning on the downstream tasks which is not
only time-consuming but also involves significant resources and access to
fine-tuning data. Prompting (Schick and Sch\"utze, 2020) has been an
alternative to fine-tuning in many NLP tasks. In our work, we explore the idea
of using prompting for response generation in task-oriented dialog systems.
Specifically, we propose an approach that performs contextual dynamic prompting
where the prompts are learnt from dialog contexts. We aim to distill useful
prompting signals from the dialog context. On experiments with MultiWOZ 2.2
dataset (Zang et al., 2020), we show that contextual dynamic prompts improve
response generation in terms of combined score (Mehri et al., 2019) by 3
absolute points, and a massive 20 points when dialog states are incorporated.
Furthermore, human annotation on these conversations found that agents which
incorporate context were preferred over agents with vanilla prefix-tuning.
