The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant text. As LLMs increasingly power conversational agents, the synthesized personality embedded in these models by virtue of their training on large amounts of human-generated data draws attention. Since personality is an important factor determining the effectiveness of communication, we present a comprehensive method for administering validated psychometric tests and quantifying, analyzing, and shaping personality traits exhibited in text generated from widely-used LLMs. We find that: 1) personality simulated in the outputs of some LLMs (under specific prompting configurations) is reliable and valid; 2) evidence of reliability and validity of LLM-simulated 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 personality profiles. We also discuss potential applications and ethical implications of our measurement and shaping framework, especially regarding responsible use of LLMs.

翻译：大型语言模型（LLMs）的出现彻底革新了自然语言处理领域，使其能够生成连贯且上下文相关的文本。随着LLMs日益成为对话系统的核心驱动力，这些模型通过大规模人类生成数据训练所内化的合成人格特质引起了广泛关注。由于人格是决定沟通效果的重要因素，我们提出了一套综合方法，用于实施经过验证的心理测量测试，并量化、分析及塑造通用LLMs输出文本中呈现的人格特质。研究发现：1）部分LLMs（在特定提示配置下）输出中模拟的人格具有可靠性和有效性；2）更大规模及经过指令微调的模型，其LLM模拟人格的可靠性与有效性证据更为显著；3）可通过沿目标维度调整LLM输出中的人格特质，以模拟特定人格特征。我们还讨论了本研究提出的测量与塑造框架的潜在应用及伦理影响，尤其关注LLMs的负责任使用问题。