Large language models (LLMs) have gained significant traction across a wide range of fields in recent years. There is also a growing expectation for them to display human-like personalities during interactions. To meet this expectation, numerous studies have proposed methods for modelling LLM personalities through psychometric evaluations. However, most existing models face two major limitations: they rely on the Big Five (OCEAN) framework, which only provides coarse personality dimensions, and they lack mechanisms for controlling trait intensity. In this paper, we address this gap by extending the Machine Personality Inventory (MPI), which originally used the Big Five model, to incorporate the 16 Personality Factor (16PF) model, allowing expressive control over sixteen distinct traits. We also developed a structured framework known as Specific Attribute Control (SAC) for evaluating and dynamically inducing trait intensity in LLMs. Our method introduces adjective-based semantic anchoring to guide trait intensity expression and leverages behavioural questions across five intensity factors: \textit{Frequency}, \textit{Depth}, \textit{Threshold}, \textit{Effort}, and \textit{Willingness}. Through experimentation, we find that modelling intensity as a continuous spectrum yields substantially more consistent and controllable personality expression compared to binary trait toggling. Moreover, we observe that changes in target trait intensity systematically influence closely related traits in psychologically coherent directions, suggesting that LLMs internalize multi-dimensional personality structures rather than treating traits in isolation. Our work opens new pathways for controlled and nuanced human-machine interactions in domains such as healthcare, education, and interviewing processes, bringing us one step closer to truly human-like social machines.

翻译：近年来，大语言模型（LLMs）在众多领域获得了广泛应用。人们也日益期望其在交互过程中展现出类人的人格特质。为满足这一期望，大量研究提出了通过心理测量评估来建模LLM人格的方法。然而，现有模型大多面临两大局限：其依赖于仅提供粗略人格维度的"大五"（OCEAN）框架，且缺乏控制特质强度的机制。本文通过扩展原本基于"大五"模型的机器人格量表（MPI），将其与16种人格因素（16PF）模型相结合，从而填补了这一空白，实现了对十六种不同特质的表达性控制。我们还开发了一个称为特定属性控制（SAC）的结构化框架，用于评估并动态诱导LLMs中的特质强度。我们的方法引入了基于形容词的语义锚定来引导特质强度表达，并利用跨越五个强度因子——\textit{频率}、\textit{深度}、\textit{阈值}、\textit{努力程度}和\textit{意愿}——的行为问题。实验表明，与二元特质切换相比，将强度建模为一个连续谱系能产生显著更一致且可控的人格表达。此外，我们观察到目标特质强度的变化会系统性地以心理上一致的方向影响密切相关的特质，这表明LLMs内化了多维人格结构，而非孤立地处理各个特质。我们的工作为医疗保健、教育和面试流程等领域中实现受控且细致入微的人机交互开辟了新途径，使我们向真正类人的社交机器又迈进了一步。