Inductive-Deductive Strategy Reuse for Multi-Turn Instructional
  Dialogues
Jiao Ou Jiayu Wu Che Liu Fuzheng Zhang Di Zhang Kun Gai
Aligning large language models (LLMs) with human expectations requires
high-quality instructional dialogues, which can be achieved by raising diverse,
in-depth, and insightful instructions that deepen interactions. Existing
methods target instructions from real instruction dialogues as a learning goal
and fine-tune a user simulator for posing instructions. However, the user
simulator struggles to implicitly model complex dialogue flows and pose
high-quality instructions. In this paper, we take inspiration from the
cognitive abilities inherent in human learning and propose the explicit
modeling of complex dialogue flows through instructional strategy reuse.
Specifically, we first induce high-level strategies from various real
instruction dialogues. These strategies are applied to new dialogue scenarios
deductively, where the instructional strategies facilitate high-quality
instructions. Experimental results show that our method can generate diverse,
in-depth, and insightful instructions for a given dialogue history. The
constructed multi-turn instructional dialogues can outperform competitive
baselines on the downstream chat model.