  Prompt-tuning has become an increasingly popular parameter-efficient method
for adapting large pretrained language models to downstream tasks. However,
both discrete prompting and continuous prompting assume fixed prompts for all
data samples within a task, neglecting the fact that inputs vary greatly in
some tasks such as open-domain dialogue generation. In this paper, we present a
novel, instance-specific prompt-tuning algorithm for dialogue generation.
Specifically, we generate prompts based on instance-level control code, rather
than the conversation history, to explore their impact on controlled dialogue
generation. Experiments on popular open-domain dialogue datasets, evaluated on
both automated metrics and human evaluation, demonstrate that our method is
superior to prompting baselines and comparable to fine-tuning with only 5%-6%
of total parameters.
