  Large language models exhibit aspects of human-level intelligence that
catalyze their application as human-like agents in domains such as social
simulations, human-machine interactions, and collaborative multi-agent systems.
However, the absence of distinct personalities, such as displaying ingratiating
behaviors, inconsistent opinions, and uniform response patterns, diminish LLMs
utility in practical applications. Addressing this, the development of
personality traits in LLMs emerges as a crucial area of research to unlock
their latent potential. Existing methods to personify LLMs generally involve
strategies like employing stylized training data for instruction tuning or
using prompt engineering to simulate different personalities. These methods
only capture superficial linguistic styles instead of the core of personalities
and are therefore not stable. In this study, we propose PersLLM, integrating
psychology-grounded principles of personality: social practice, consistency,
and dynamic development, into a comprehensive training methodology. We
incorporate personality traits directly into the model parameters, enhancing
the model's resistance to induction, promoting consistency, and supporting the
dynamic evolution of personality. Single-agent evaluation validates our
method's superiority, as it produces responses more aligned with reference
personalities compared to other approaches. Case studies for multi-agent
communication highlight its benefits in enhancing opinion consistency within
individual agents and fostering collaborative creativity among multiple agents
in dialogue contexts, potentially benefiting human simulation and multi-agent
cooperation. Additionally, human-agent interaction evaluations indicate that
our personified models significantly enhance interactive experiences,
underscoring the practical implications of our research.
