给大模型注入人格后，其认知能力会系统性变化，可动态调度以提升性能。
A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities
- 用神经元层面方法注入五大性格特质，使模型行为更贴近真实人格
- 开放性和外向性对任务表现影响最显著，73.68%与人类规律一致
- 提出动态人格路由策略，无需训练即可超越固定人格表现
为定制交互风格，向大语言模型（LLMs）注入特定人格已成普遍做法，但其对底层认知能力的影响尚不明确。本文采用基于神经元的人格特质诱导（NPTI）框架，在LLM中引入五大性格特质，并在六个认知基准上评估性能。结果表明，人格诱导不仅带来稳定的行为变化，还引发认知任务表现的系统性迁移，且效果具有强任务依赖性：某些人格在指令遵循任务中表现提升，而另一些则损害复杂推理能力。效应幅度随性格维度系统变化，其中开放性与外向性影响最为显著。此外，模型表现与人类性格-认知关系的定向一致性达73.68%。基于此规律，本文提出轻量级查询自适应策略动态人格路由（DPR），在不增加训练成本的前提下，优于最佳静态人格设定。
原文摘要 · Abstract (English)
Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capabilities remains unexplored. We employ the Neuron-based Personality Trait Induction (NPTI) framework to induce Big Five personality traits in LLMs and evaluate performance across six cognitive benchmarks. Our findings reveal that persona induction produces stable, reproducible shifts in cognitive task performance beyond surface-level stylistic changes. These effects exhibit strong task dependence: certain personalities yield consistent gains on instruction-following, while others impair complex reasoning. Effect magnitude varies systematically by trait dimension, with Openness and Extraversion exerting the most robust influence. Furthermore, LLM effects show 73.68% directional consistency with human personality-cognition relationships. Capitalizing on these regularities, we propose Dynamic Persona Routing (DPR), a lightweight query-adaptive strategy that outperforms the best static persona without additional training.
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