大型语言模型中的个性特质
Personality Traits in Large Language Models
AI总结：
本文提出一种心理测量学上有效可靠的方法，用于在18个大型语言模型上测量、验证并塑造合成个性，发现较大且经指令微调的模型个性信效度更强，并探讨了其对负责任AI的伦理意义。
摘要：
大型语言模型（LLMs）的出现彻底变革了自然语言处理，使生成连贯且与语境相关、类似人类的文本成为可能。随着LLMs日益驱动全球公众使用的对话智能体，这些模型因在大规模人类数据上训练而内嵌的合成个性特质正变得越来越重要。由于个性是决定沟通有效性的关键因素，我们提出了一种新颖且全面的、在心理测量学上有效且可靠的方法，用于在广泛使用的LLMs上施测和验证个性测试，并用于在此类LLMs生成的文本中塑造个性。将这一方法应用于18个LLMs后，我们发现：1）在特定提示配置下，部分LLMs输出中的个性测量结果是可靠且有效的；2）对于规模更大且经过指令微调的模型，合成LLM个性的信度和效度证据更强；3）LLMs输出中的个性可以沿所需维度进行塑造，以模仿特定的人类个性画像。我们讨论了该测量与塑造方法的应用及伦理影响，特别是关于负责任的人工智能方面。
英文摘要：
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 powerconversational agents used by the general public world-wide, the synthetic personality traits 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 novel and comprehensive psychometrically valid and reliable methodology 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 to 18 LLMs, 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 the application and ethical implications of the measurement and shaping method, in particular regarding responsible AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
