Thinking Before Speaking: A Role-playing Model with Mindset
This addresses a specific limitation in AI role-playing for applications like chatbots or simulations, but it is incremental as it builds on existing fine-tuning and prompting methods.
The paper tackles the problem of large language models (LLMs) performing poorly in role-playing when faced with knowledge gaps or role-specific logic, proposing a Thinking Before Speaking (TBS) model that fine-tunes LLMs with mindset-supplemented data to better emulate roles in tone, knowledge, and mindset.
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.