Recent Large Language Models (LLMs) have shown remarkable capabilities in
mimicking fictional characters or real humans in conversational settings.
However, the realism and consistency of these responses can be further enhanced
by providing richer information of the agent being mimicked. In this paper, we
propose a novel approach to generate more realistic and consistent responses
from LLMs, leveraging five senses, attributes, emotional states, relationship
with the interlocutor, and memories. By incorporating these factors, we aim to
increase the LLM's capacity for generating natural and realistic reactions in
conversational exchanges. Through our research, we expect to contribute to the
development of LLMs that demonstrate improved capabilities in mimicking
fictional characters. We release a new benchmark dataset and all our codes,
prompts, and sample results on our Github:
this https URL

借助五感、属性、情感状态、与交互者的关系和记忆，我们提出了一种新的方法来从大型语言模型中生成更加真实、一致的回答，旨在增强大型语言模型在会话交流中生成自然、真实反应的能力。通过我们的研究，我们希望为模仿小说人物的大型语言模型的改进能力做出贡献。我们在我们的 GitHub 上发布了一个新的基准数据集以及所有的代码、提示和样例结果。