大型语言模型中的个性特征

摘要

大型语言模型（LLM）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符合语境的文本。随着LLM日益成为对话系统的核心驱动力，这些模型通过海量人类生成数据训练所内嵌的合成人格特质引发关注。鉴于人格是决定沟通效能的关键因素，我们提出一套综合方法，通过实施经过验证的心理测量测试，对主流LLM生成文本中呈现的人格特质进行量化、分析与塑造。研究发现：1）特定提示配置下，部分LLM输出中模拟的人格具有可靠性与有效性；2）规模更大且经过指令微调的模型，其人格模拟的可靠性与有效性证据更为充分；3）LLM输出的人格可沿特定维度进行塑造，以模拟目标人格特征。本文还探讨了该测量与塑造框架的潜在应用及伦理影响，特别是在LLM的责任使用方面。

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.

大型语言模型中的个性特征

Personality Traits in Large Language Models

摘要

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