Large Language Models (LLMs) are often misleadingly recognized as having a personality or a set of values. We argue that an LLM can be seen as a superposition of perspectives with different values and personality traits. LLMs exhibit context-dependent values and personality traits that change based on the induced perspective (as opposed to humans, who tend to have more coherent values and personality traits across contexts). We introduce the concept of perspective controllability, which refers to a model's affordance to adopt various perspectives with differing values and personality traits. In our experiments, we use questionnaires from psychology (PVQ, VSM, IPIP) to study how exhibited values and personality traits change based on different perspectives. Through qualitative experiments, we show that LLMs express different values when those are (implicitly or explicitly) implied in the prompt, and that LLMs express different values even when those are not obviously implied (demonstrating their context-dependent nature). We then conduct quantitative experiments to study the controllability of different models (GPT-4, GPT-3.5, OpenAssistant, StableVicuna, StableLM), the effectiveness of various methods for inducing perspectives, and the smoothness of the models' drivability. We conclude by examining the broader implications of our work and outline a variety of associated scientific questions. The project website is available at https://sites.google.com/view/llm-superpositions .

翻译：大型语言模型（LLM）常被误解为具有个性或一套固定价值观。我们认为，LLM可被视为不同价值观和人格特质视角的叠加态。LLM表现出依赖上下文的价值观与人格特质，这些特质会随诱导视角的变化而改变（与人类通常在不同情境下保持更一致价值观和人格特质相反）。我们提出“视角可控性”概念，指模型采用不同价值观和人格特质视角的供能能力。实验中，我们采用心理学问卷（PVQ、VSM、IPIP）研究不同视角下所呈现价值观与人格特质的变化。通过定性实验，我们证明LLM会在提示中（显性或隐性）暗示这些价值观时表达不同价值观，且即便未明显暗示时也会表达不同价值观（体现其上下文依赖性）。随后，我们开展定量实验研究不同模型（GPT-4、GPT-3.5、OpenAssistant、StableVicuna、StableLM）的可控性、诱发视角方法的有效性，以及模型驱动能力的平滑性。最后，我们探讨本研究的广泛影响，并概述一系列相关科学问题。项目网站见https://sites.google.com/view/llm-superpositions。