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（LLMs）常被误认为具有独立人格或一套固有价值观。我们提出，LLMs可被视作包含不同价值观和人格特质的多重视角叠加。与人类在跨情境中通常保持较稳定价值观与人格特质不同，LLMs会随诱导视角的变化展现出情境依赖的价值观与人格特质。我们引入"视角可控性"概念，指模型采纳不同价值观和人格特质视角的能力。实验中，我们采用心理学问卷（PVQ、VSM、IPIP）研究显性价值观与人格特质如何随视角变化。通过定性实验表明，LLMs在提示中（显性或隐性）暗示价值观时会表达不同取向，即便未明显暗示时也会呈现差异（体现其情境依赖性）。随后开展定量实验，研究不同模型（GPT-4、GPT-3.5、OpenAssistant、StableVicuna、StableLM）的可控性、视角诱导方法的效果及模型驱动的平滑性。最后，我们探讨了该研究的广泛影响，并概述了相关科学问题。项目网站见https://sites.google.com/view/llm-superpositions。