Controllable Mixed-Initiative Dialogue Generation through Prompting
Maximillian Chen, Xiao Yu, Weiyan Shi, Urvi Awasthi, Yu Zhou · 2023
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 finetuning on conditional generation.We formalize prompt construction for controllable mixedinitiative dialogue.Our findings show improvements over fine-tuning and ground truth responses according to human evaluation and automatic metrics for two tasks: PersuasionFor-Good and Emotional Support Conversations.