Personality, a fundamental aspect of human cognition, contains a range of
traits that influence behaviors, thoughts, and emotions. This paper explores
the capabilities of large language models (LLMs) in reconstructing these
complex cognitive attributes based only on simple descriptions containing
socio-demographic and personality type information. Utilizing the HEXACO
personality framework, our study examines the consistency of LLMs in recovering
and predicting underlying (latent) personality dimensions from simple
descriptions. Our experiments reveal a significant degree of consistency in
personality reconstruction, although some inconsistencies and biases, such as a
tendency to default to positive traits in the absence of explicit information,
are also observed. Additionally, socio-demographic factors like age and number
of children were found to influence the reconstructed personality dimensions.
These findings have implications for building sophisticated agent-based
simulacra using LLMs and highlight the need for further research on robust
personality generation in LLMs.

本文研究借助简单描述包含社会人口和个性类型信息，大型语言模型（LLMs）在重建人类认知中基本性格特征方面的能力。通过利用 HEXACO 人格框架，我们的研究探讨了 LLMs 在从简单描述中恢复和预测潜在人格维度的一致性。实验证明在人格重建方面存在显著的一致性，但也观察到一些不一致性和偏见，例如在缺乏明确信息时默认为积极特质。此外，年龄和子女数量等社会人口因素也会影响恢复的人格维度。这些发现对于利用 LLMs 构建复杂的基于代理的模拟具有重要意义，并强调了对 LLMs 中健壮人格生成的进一步研究的需求。

个性是否仅限于角色扮演？使用 ChatGPT 从简单描述中重构智能体的潜在个性

Is persona enough for personality? Using ChatGPT to reconstruct an  agent's latent personality from simple descriptions

Since Labov's (1964) foundational work on the social stratification of
language, linguistics has dedicated concerted efforts towards understanding the
relationships between socio-demographic factors and language production and
perception. Despite the large body of evidence identifying significant
relationships between socio-demographic factors and language production,
relatively few of these factors have been investigated in the context of NLP
technology. While age and gender are well covered, Labov's initial target,
socio-economic class, is largely absent. We survey the existing Natural
Language Processing (NLP) literature and find that only 20 papers even mention
socio-economic status. However, the majority of those papers do not engage with
class beyond collecting information of annotator-demographics. Given this
research lacuna, we provide a definition of class that can be operationalised
by NLP researchers, and argue for including socio-economic class in future
language technologies.

在自然语言处理技术中，虽然已经研究了年龄和性别等方面与语言产生之间的关系，但至今较少研究 社会经济阶级等因素。本研究通过对现有自然语言处理文献进行调查，发现只有 20 篇论文提及社会经济阶级，并且大多数论文仅限于收集注释者人口统计信息，而未深入研究该阶级。鉴于此项研究空白，我们提供了一个可在自然语言处理研究中操作的社会经济阶级定义，并主张在今后的语言技术中应包含社会经济阶级。