Thinking Before Speaking: A Role-playing Model with Mindset
Baohua Zhang, Yongyi Huang, Wenyao Cui, Huaping Zhang

TL;DR
This paper introduces a Thinking Before Speaking (TBS) model that enhances Large Language Models' ability to adopt authentic role-specific mindsets and reasoning by augmenting training data with character-specific scenarios and thought processes.
Contribution
The study proposes a novel data augmentation and fine-tuning approach to improve LLMs' role-playing authenticity by incorporating character mindset and beyond-knowledge elements.
Findings
TBS model better emulates role tone and mindset
Enhanced role-specific responses outside the model's knowledge base
Improved consistency with character scenarios
Abstract
Role-playing is an easy task for Large Language Models (LLMs), as they are skilled at simulating human behaviors. Many current studies have enabled LLMs to generate responses in the tone of a specific role by fine-tuning the models or using specialized prompts. However, it is typically easy to recognize when a role is being played by LLMs. These models tend to perform poorly when confronted with knowledge that the assumed role does not possess, or a question that requires the specific experience or logic of the role to answer. To address this problem and make LLMs act more like real roles, we propose a Thinking Before Speaking (TBS) model in this paper. Unlike other studies, we first extend the data based on the character's real-life scenarios and the historical dialogue, supplementing each pair of dialogue with the character's mindset. Then we add few data points that include elements…
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Taxonomy
TopicsCognitive Science and Mapping · Complex Systems and Decision Making
