让大模型先思考角色心态，更真实地扮演特定人物。
Thinking Before Speaking: A Role-playing Model with Mindset
- 通过补充角色真实情境和心理状态数据，引导模型模仿角色思维逻辑。
- 实验显示模型在语气、知识和心态上均更贴近目标角色。
- 适合需要深度角色扮演的对话系统与虚拟人应用。
角色扮演对大语言模型而言是简单任务，因其擅长模拟人类行为。现有研究多通过微调或特殊提示使模型以特定角色口吻回应，但易被识破——当问题超出该角色知识范围或需角色特有经验时，模型表现不佳。为提升真实性，本文提出‘思考后再说话’（TBS）模型：首先基于角色真实生活场景与历史对话，补充每对对话中的角色心态；再引入少量超越角色知识边界的数据点进行微调。此方法帮助模型内化角色思维过程与逻辑，避免脱离角色知识的回答。我们还构建了专用数据集与评估指标。实验表明，TBS模型在语气、知识和心态方面均更成功地模拟了目标角色。
原文摘要 · Abstract (English)
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 beyond the role's knowledge, and fine-tune the LLMs. This approach can help LLMs adopt the role's thought process and logic, avoiding responses that fall outside the role's knowledge base. We have also prepared a dataset and evaluation metrics to test these capabilities. Experimental results show that our TBS model can better emulate a role in terms of tone, knowledge, and mindset.
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