CharacterChat: Learning towards Conversational AI with Personalized
  Social Support
Quan Tu Chuanqi Chen Jinpeng Li Yanran Li Shuo Shang Dongyan Zhao Ran Wang Rui Yan
In our modern, fast-paced, and interconnected world, the importance of mental
well-being has grown into a matter of great urgency. However, traditional
methods such as Emotional Support Conversations (ESC) face challenges in
effectively addressing a diverse range of individual personalities. In
response, we introduce the Social Support Conversation (S2Conv) framework. It
comprises a series of support agents and the interpersonal matching mechanism,
linking individuals with persona-compatible virtual supporters. Utilizing
persona decomposition based on the MBTI (Myers-Briggs Type Indicator), we have
created the MBTI-1024 Bank, a group that of virtual characters with distinct
profiles. Through improved role-playing prompts with behavior preset and
dynamic memory, we facilitate the development of the MBTI-S2Conv dataset, which
contains conversations between the characters in the MBTI-1024 Bank. Building
upon these foundations, we present CharacterChat, a comprehensive S2Conv
system, which includes a conversational model driven by personas and memories,
along with an interpersonal matching plugin model that dispatches the optimal
supporters from the MBTI-1024 Bank for individuals with specific personas.
Empirical results indicate the remarkable efficacy of CharacterChat in
providing personalized social support and highlight the substantial advantages
derived from interpersonal matching. The source code is available in
\url{https://github.com/morecry/CharacterChat}.