大型语言模型中的人格特征

摘要

大型语言模型（LLMs）的出现彻底改变了自然语言处理，使得生成连贯且上下文相关的文本成为可能。随着LLMs越来越多地驱动会话代理，这些模型中蕴含的合成个性，由于它们在大量人类生成数据上训练，引起了人们的关注。由于个性是决定沟通效果的重要因素，我们提出了一种全面的方法，用于进行经过验证的心理测量测试，并量化、分析和塑造从广泛使用的LLMs生成的文本中展现的个性特征。我们发现：1）在某些LLMs的输出中模拟的个性（在特定提示配置下）是可靠且有效的；2）LLM模拟的个性的可靠性和有效性证据对于更大和经过指导微调的模型更为强大；3）LLMs输出中的个性可以沿着期望的维度塑造，以模仿特定的个性特征。我们还讨论了我们的测量和塑造框架的潜在应用和伦理影响，特别是关于负责任地使用LLMs的问题。

English

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.

大型语言模型中的人格特征

Personality Traits in Large Language Models

摘要

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