CharacterGLM: Customizing Chinese Conversational AI Characters with
  Large Language Models
Jinfeng Zhou Zhuang Chen Dazhen Wan Bosi Wen Yi Song Jifan Yu Yongkang Huang Libiao Peng Jiaming Yang Xiyao Xiao Sahand Sabour Xiaohan Zhang Wenjing Hou Yijia Zhang Yuxiao Dong Jie Tang Minlie Huang
In this paper, we present CharacterGLM, a series of models built upon
ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM
is designed for generating Character-based Dialogues (CharacterDial), which
aims to equip a conversational AI system with character customization for
satisfying people's inherent social desires and emotional needs. On top of
CharacterGLM, we can customize various AI characters or social agents by
configuring their attributes (identities, interests, viewpoints, experiences,
achievements, social relationships, etc.) and behaviors (linguistic features,
emotional expressions, interaction patterns, etc.). Our model outperforms most
mainstream close-source large langauge models, including the GPT series,
especially in terms of consistency, human-likeness, and engagement according to
manual evaluations. We will release our 6B version of CharacterGLM and a subset
of training data to facilitate further research development in the direction of
character-based dialogue generation.