  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.
