  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
https://github.com/Bernard-Yang/SimsChat.
