Wenyao Cui

Baohua Zhang, Yongyi Huang, Wenyao Cui et al.

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.

Baohua Zhan, Yongyi Huang, Wenyao Cui et al.

Questionnaires are a common method for detecting the personality of Large Language Models (LLMs). However, their reliability is often compromised by two main issues: hallucinations (where LLMs produce inaccurate or irrelevant responses) and the sensitivity of responses to the order of the presented options. To address these issues, we propose combining text mining with questionnaires method. Text mining can extract psychological features from the LLMs' responses without being affected by the order of options. Furthermore, because this method does not rely on specific answers, it reduces the influence of hallucinations. By normalizing the scores from both methods and calculating the root mean square error, our experiment results confirm the effectiveness of this approach. To further investigate the origins of personality traits in LLMs, we conduct experiments on both pre-trained language models (PLMs), such as BERT and GPT, as well as conversational models (ChatLLMs), such as ChatGPT. The results show that LLMs do contain certain personalities, for example, ChatGPT and ChatGLM exhibit the personality traits of 'Conscientiousness'. Additionally, we find that the personalities of LLMs are derived from their pre-trained data. The instruction data used to train ChatLLMs can enhance the generation of data containing personalities and expose their hidden personality. We compare the results with the human average personality score, and we find that the personality of FLAN-T5 in PLMs and ChatGPT in ChatLLMs is more similar to that of a human, with score differences of 0.34 and 0.22, respectively.