In the realm of mimicking human deliberation, large language models (LLMs) show promising performance, thereby amplifying the importance of this research area. Deliberation is influenced by both logic and personality. However, previous studies predominantly focused on the logic of LLMs, neglecting the exploration of personality aspects. In this work, we introduce Dynamic Personality Generation (DPG), a dynamic personality generation method based on Hypernetworks. Initially, we embed the Big Five personality theory into GPT-4 to form a personality assessment machine, enabling it to evaluate characters' personality traits from dialogues automatically. We propose a new metric to assess personality generation capability based on this evaluation method. Then, we use this personality assessment machine to evaluate dialogues in script data, resulting in a personality-dialogue dataset. Finally, we fine-tune DPG on the personality-dialogue dataset. Experiments prove that DPG's personality generation capability is stronger after fine-tuning on this dataset than traditional fine-tuning methods, surpassing prompt-based GPT-4.

翻译：在模拟人类深思熟虑的领域中，大型语言模型展现了卓越性能，从而凸显了这一研究领域的重要性。深思熟虑既受逻辑影响，也受性格影响。然而，以往研究主要聚焦于大型语言模型的逻辑能力，忽视了对性格层面的探索。本研究提出了一种基于超网络的动态性格生成方法DPG（Dynamic Personality Generation）。首先，我们将大五人格理论嵌入GPT-4，构建了一个性格评估器，使其能够从对话中自动评估角色的性格特质。基于该评估方法，我们提出了一种新的指标来衡量性格生成能力。随后，利用该性格评估器对剧本数据中的对话进行评估，构建了一个性格-对话数据集。最后，我们在此数据集上对DPG进行微调。实验证明，相较于传统微调方法，DPG在该数据集上微调后展现出更强的性格生成能力，甚至超越了基于prompt的GPT-4。