Role-playing has wide-ranging applications in customer support, embodied agents, computational social science, etc. The influence of parametric world knowledge of large language models (LLMs) often causes role-playing characters to act out of character and hallucinate about things outside the scope of their knowledge. In this work, we focus on the evaluation and mitigation of hallucination in fictional character role-play. We introduce a dataset with more than 2,000 characters and 72,000 interviews, including 18,000 adversarial questions. We propose RoleFact, a role-playing method that mitigates hallucination by modulating the influence of parametric knowledge using a pre-calibrated confidence threshold. Experiments show that the proposed method improves the factual precision of generated responses by 18% for adversarial questions with a 44% reduction in temporal hallucination for time-sensitive interviews. The code and the dataset will be available at https://github.com/NafisSadeq/rolefact.git.

我们研究了虚构角色角色扮演中幻觉的评估和减轻方法，提出了一种名为RoleFact的角色扮演方法，通过调节参数化知识的影响来减轻幻觉，并且实验证明该方法在对抗性问题的实际准确性提高了18％，并且对于时间敏感的访谈减少了44％的时间幻觉。

缓解虚构角色扮演中的幻觉