  Mixed-initiative dialogue tasks involve repeated exchanges of information and
conversational control. Conversational agents gain control by generating
responses that follow particular dialogue intents or strategies, prescribed by
a policy planner. The standard approach has been fine-tuning pre-trained
language models to perform generation conditioned on these intents. However,
these supervised generation models are limited by the cost and quality of data
annotation. We instead prompt large language models as a drop-in replacement to
fine-tuning on conditional generation. We formalize prompt construction for
controllable mixed-initiative dialogue. Our findings show improvements over
fine-tuning and ground truth responses according to human evaluation and
automatic metrics for two tasks: PersuasionForGood and Emotional Support
Conversations.
