As large language models (LLMs) appear to behave increasingly human-like in text-based interactions, more and more researchers become interested in investigating personality in LLMs. However, the diversity of psychological personality research and the rapid development of LLMs have led to a broad yet fragmented landscape of studies in this interdisciplinary field. Extensive studies across different research focuses, different personality psychometrics, and different LLMs make it challenging to have a holistic overview and further pose difficulties in applying findings to real-world applications. In this paper, we present a comprehensive review by categorizing current studies into three research problems: self-assessment, exhibition, and recognition, based on the intrinsic characteristics and external manifestations of personality in LLMs. For each problem, we provide a thorough analysis and conduct in-depth comparisons of their corresponding solutions. Besides, we summarize research findings and open challenges from current studies and further discuss their underlying causes. We also collect extensive publicly available resources to facilitate interested researchers and developers. Lastly, we discuss the potential future research directions and application scenarios. Our paper is the first comprehensive survey of up-to-date literature on personality in LLMs. By presenting a clear taxonomy, in-depth analysis, promising future directions, and extensive resource collections, we aim to provide a better understanding and facilitate further advancements in this emerging field.

翻译：随着大语言模型（LLMs）在基于文本的交互中表现出越来越强的类人行为，越来越多的研究者开始关注LLMs中的人格特质研究。然而，心理学人格研究的多样性与LLMs的快速发展，导致这一交叉领域的研究呈现广泛而碎片化的局面。不同研究焦点、不同人格心理测量工具以及不同LLMs的大量研究，使得整体概览变得困难，并进一步导致研究成果在实际应用中的转化面临挑战。本文通过基于LLMs人格的内在特性与外在表现，将现有研究归纳为三个核心问题：自我评估、表现与识别，并以此展开系统性综述。针对每个问题，我们进行了深入分析，并对相应解决方案进行了详细比较。此外，我们总结了现有研究的主要发现与开放挑战，并进一步探讨其内在成因。我们还整理了丰富的公开可用资源，以方便相关研究者与开发者使用。最后，我们探讨了未来潜在的研究方向与应用场景。本文是首篇对LLMs人格研究最新文献的全面综述。通过提出清晰的分类框架、深入的分析、前瞻性的未来方向以及丰富的资源汇总，我们旨在增进对该新兴领域的理解，并推动其进一步发展。