Estimating the Personality of White-Box Language Models (2204.12000v2)

Abstract: Technology for open-ended language generation, a key application of artificial intelligence, has advanced to a great extent in recent years. Large-scale LLMs, 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 LLMs 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 LLMs 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.