  Large language models (LLMs) exhibit robust capabilities in text generation
and comprehension, mimicking human behavior and exhibiting synthetic
personalities. However, some LLMs have displayed offensive personality,
propagating toxic discourse. Existing literature neglects the origin and
evolution of LLM personalities, as well as the effective personality control.
To fill these gaps, our study embarked on a comprehensive investigation into
LLM personality control. We investigated several typical methods to influence
LLMs, including three training methods: Continual Pre-training, Supervised
Fine-Tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF), along
with inference phase considerations (prompts). Our investigation revealed a
hierarchy of effectiveness in control: Prompt > SFT > RLHF > Continual
Pre-train. Notably, SFT exhibits a higher control success rate compared to
prompt induction. While prompts prove highly effective, we found that
prompt-induced personalities are less robust than those trained, making them
more prone to showing conflicting personalities under reverse personality
prompt induction. Besides, harnessing the strengths of both SFT and prompt, we
proposed $\underline{\text{P}}$rompt $\underline{\text{I}}$nduction post
$\underline{\text{S}}$upervised $\underline{\text{F}}$ine-tuning (PISF), which
emerges as the most effective and robust strategy for controlling LLMs'
personality, displaying high efficacy, high success rates, and high robustness.
Even under reverse personality prompt induction, LLMs controlled by PISF still
exhibit stable and robust personalities.
