The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly power conversational agents used by the general public world-wide, the synthetic personality embedded in these models, by virtue of training on large amounts of human data, is becoming increasingly important. Since personality is a key factor determining the effectiveness of communication, we present a comprehensive method for administering and validating personality tests on widely-used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method, we found: 1) personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; 2) evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction fine-tuned models; and 3) personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss application and ethical implications of the measurement and shaping method, in particular regarding responsible AI.

翻译：大型语言模型（LLMs）的出现彻底改变了自然语言处理领域，使其能够生成连贯且与上下文相关的人类化文本。随着LLMs日益驱动着全球公众广泛使用的对话代理，这些模型通过大量人类数据训练而蕴含的合成人格变得越来越重要。由于人格是决定沟通有效性的关键因素，我们提出了一种综合方法，用于对广泛使用的LLMs进行人格测试的实施与验证，并塑造此类模型生成文本中的人格。应用该方法，我们发现：1）某些LLMs在特定提示配置下的输出结果中，人格测量具有可靠性和有效性；2）对于规模更大且经过指令微调的模型，其合成LLM人格的可靠性与有效性证据更为显著；3）LLM输出中的人格可沿所需维度进行塑造，以模仿特定人类人格特征。我们讨论了测量与塑造方法的应用及伦理影响，特别是涉及负责任人工智能方面。