StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized
  Dialogue Generation
Jinpeng Li Zekai Zhang Quan Tu Xin Cheng Dongyan Zhao Rui Yan
Large Language Models (LLMs) demonstrate superior performance in generative
scenarios and have attracted widespread attention. Among them, stylized
dialogue generation is essential in the context of LLMs for building
intelligent and engaging dialogue agent. However the ability of LLMs is
data-driven and limited by data bias, leading to poor performance on specific
tasks. In particular, stylized dialogue generation suffers from a severe lack
of supervised data. Furthermore, although many prompt-based methods have been
proposed to accomplish specific tasks, their performance in complex real-world
scenarios involving a wide variety of dialog styles further enhancement. In
this work, we first introduce a stylized dialogue dataset StyleEval with 38
styles by leveraging the generative power of LLMs comprehensively, which has
been carefully constructed with rigorous human-led quality control. Based on
this, we propose the stylized dialogue framework StyleChat via
recitation-augmented memory strategy and multi-task style learning strategy to
promote generalization ability. To evaluate the effectiveness of our approach,
we created a test benchmark that included both a generation task and a choice
task to comprehensively evaluate trained models and assess whether styles and
preferences are remembered and understood. Experimental results show that our
proposed framework StyleChat outperforms all the baselines and helps to break
the style boundary of LLMs.