自我评估、表现与识别：大型语言模型中人格的综述
Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models
AI总结：
本文综述了大型语言模型中人格的研究，将其归纳为自我评估、表现与识别三大问题，通过深入分析、比较现有方案并汇总资源，为该跨学科领域提供了首份系统性文献综述。
摘要：
随着大型语言模型（LLMs）在基于文本的交互中表现得越来越像人类，越来越多的研究者开始对研究LLMs中的人格产生兴趣。然而，心理学人格研究的多样性以及LLMs的快速发展，导致这一跨学科领域的研究呈现出广泛却碎片化的局面。跨越不同研究重点、不同人格心理测量方法和不同LLMs的大量研究，使得获得一个整体性的概述变得具有挑战性，并进一步给将研究发现应用于现实世界的应用带来了困难。在本文中，我们根据LLMs中人格的内在特征和外在表现，将当前的研究分为三个研究问题进行全面的综述：自我评估、表现和识别。针对每个问题，我们提供了深入的分析，并对其相应的解决方案进行了深入比较。此外，我们总结了当前研究的研究发现和开放性挑战，并进一步探讨了其潜在原因。我们还收集了大量公开可用的资源，以期为感兴趣的研究者和开发者提供便利。最后，我们讨论了潜在的未来研究方向和应用场景。本文是第一篇关于LLMs中人格的最新文献的全面综述。通过呈现清晰的分类法、深入的分析、有前景的未来方向以及广泛的资源收集，我们旨在提供更好的理解并促进这一新兴领域的进一步发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
