Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning
  Framework for Dialogue
Jian Wang Chak Tou Leong Jiashuo Wang Dongding Lin Wenjie Li Xiao-Yong Wei
Tuning pretrained language models for dialogue generation has been a
prevalent paradigm for building capable dialogue agents. Yet, traditional
tuning narrowly views dialogue generation as resembling other language
generation tasks, ignoring the role disparities between two speakers and the
multi-round interactive process that dialogues ought to be. Such a manner leads
to unsatisfactory chat consistency of the built agent. In this work, we
emphasize the interactive, communicative nature of dialogue and argue that it
is more feasible to model the speaker roles of agent and user separately,
enabling the agent to adhere to its role consistently. We propose an efficient
Multi-round Interactive Dialogue Tuning (Midi-Tuning) framework. It models the
agent and user individually with two adapters built upon large language models,
where they utilize utterances round by round in alternating order and are tuned
via a round-level memory caching mechanism. Extensive experiments demonstrate
that, our framework performs superior to traditional fine-tuning and harbors
the tremendous potential for improving dialogue consistency.