Technology for open-ended language generation, a key application of artificial intelligence, has advanced to a great extent in recent years. Large-scale language models, which are trained on large corpora of text, are being used in a wide range of applications everywhere, from virtual assistants to conversational bots. While these language models output fluent text, existing research shows that these models can and do capture human biases. Many of these biases, especially those that could potentially cause harm, are being well-investigated. On the other hand, studies that infer and change human personality traits inherited by these models have been scarce or non-existent. Our work seeks to address this gap by exploring the personality traits of several large-scale language models designed for open-ended text generation and the datasets used for training them. We build on the popular Big Five factors and develop robust methods that quantify the personality traits of these models and their underlying datasets. In particular, we trigger the models with a questionnaire designed for personality assessment and subsequently classify the text responses into quantifiable traits using a Zero-shot classifier. Our estimation scheme sheds light on an important anthropomorphic element found in such AI models and can help stakeholders decide how they should be applied as well as how society could perceive them. Additionally, we examined approaches to alter these personalities, adding to our understanding of how AI models can be adapted to specific contexts.

翻译：开放文本生成技术作为人工智能的关键应用，近年来已取得显著进展。基于大规模文本语料库训练的语言模型，正被广泛应用于从虚拟助手到对话机器人的各类场景。尽管这些语言模型能生成流畅文本，但现有研究表明，它们不仅能够捕捉人类偏见，而且确实在实践层面呈现此类特征。其中具有潜在危害性的偏见类型已得到充分研究，然而针对模型继承人类人格特质进行推断与修饰的研究仍极为匮乏。本研究通过剖析多个面向开放文本生成的大规模语言模型及其训练数据集的人格特质，旨在填补这一研究空白。我们基于经典的大五人格理论框架，开发了量化模型及其底层数据集人格特征的稳健方法。具体而言，我们采用标准人格评估问卷对模型进行触发测试，随后通过零样本分类器将文本响应归类为可量化的人格维度。本评估方案揭示了此类人工智能模型中重要的拟人化特征，有助于利益相关方判断模型的适用场景及社会认知定位。此外，我们还探讨了调整这些人格特征的可行路径，进一步深化了对人工智能模型情境化适配机制的理解。