  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:
https://github.com/srafsasm/InfoRichBot
