Large Language Models (LLMs) possess the remarkable capability to understand
human instructions and generate high-quality text, enabling them to act as
agents that simulate human behaviours. This capability allows LLMs to emulate
human beings in a more advanced manner, beyond merely replicating simple human
behaviours. However, there is a lack of exploring into leveraging LLMs to craft
characters from several aspects. In this work, we introduce the Customisable
Conversation Agent Framework, which employs LLMs to simulate real-world
characters that can be freely customised according to different user
preferences. The customisable framework is helpful for designing customisable
characters and role-playing agents according to human's preferences. We first
propose the SimsConv dataset, which comprises 68 different customised
characters, 1,360 multi-turn role-playing dialogues, and encompasses 13,971
interaction dialogues in total. The characters are created from several
real-world elements, such as career, aspiration, trait, and skill. Building on
these foundations, we present SimsChat, a freely customisable role-playing
agent. It incorporates different real-world scenes and topic-specific character
interaction dialogues, simulating characters' life experiences in various
scenarios and topic-specific interactions with specific emotions. Experimental
results show that our proposed framework achieves desirable performance and
provides helpful guideline for building better simulacra of human beings in the
future. Our data and code are available at
this https URL

大型语言模型（LLMs）能够理解人类指令并生成高质量的文本，使它们能够模拟人类行为并超越简单的复制。本研究介绍了可定制的对话代理框架，利用 LLMs 模拟真实世界中可以根据用户偏好自由定制的角色。提出了 SimsConv 数据集和 SimsChat 对话代理，模拟不同情景下角色的生活经历和特定情感的专题交互。实验证明该框架取得了令人满意的性能，并为未来构建更好的人类模拟提供了有益的指导。