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（LLM）的出现彻底革新了自然语言处理，使其能够生成连贯且上下文相关的类人文本。随着LLM日益成为全球公众广泛使用的对话智能体的核心驱动力，这些模型通过大量人类数据训练所嵌入的合成人格正变得愈发重要。由于人格是决定沟通有效性的关键因素，我们提出了一套综合方法，用于对广泛使用的LLM进行人格测试的施测与验证，以及在其生成文本中塑造人格特质。应用该方法，我们发现：1）在特定提示配置下，部分LLM输出中的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输出中的人格可在所需维度上进行塑造，以模仿特定人类人格特征。我们讨论了这种测量与塑造方法的应用与伦理影响，特别是关于负责任人工智能的议题