神经语言模型中的叙述连贯性
Alessandro Acciai1, Lucia Guerrisi1, Pietro Perconti1
1Department of Cognitive Science, University of Messina, Messina, Italy.
Frontiers in psychology
|April 16, 2025
概括
像GPT-3.5和GPT-4这样的大型语言模型在自传故事中展示了类似人类的叙事连贯性. 这表明了复杂的内部知识整合,可与人类的个人自我相提并论.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 神经语言模型表现出类似人类的语言行为,表明复杂的潜在认知系统.
- 心理学理论,特别是关于叙事连贯性的理论,正在被采用来研究这些系统.
- 人类的"自我"概念与高度集成的个人知识系统有关.
研究的目的:
- 调查神经语言模型 (NLM) 语言生产中连贯整合的潜力.
- 探索NLM是否具有与人类个人身份相似的整合核心.
- 将叙事连贯性的心理理论应用于NLM生成的自传故事.
主要方法:
- 利用心理学理论,将叙事连贯性与知识系统集成联系起来.
- 应用定量方法来分析自传文本中的连贯性.
- 从GPT-3.5和GPT-4生成了4020个自传故事,不同的角色 (性别,情绪,年龄).
- 开发了一种自动化的连贯性评估程序,用心理学家分析的样本进行微调.
主要成果:
- 通过NLM生成的故事表现出与人类受试者可比的叙事连贯性水平.
- GPT-4的叙事连贯性略高于GPT-3.5.5的叙事连贯性.
- 结果表明,模型中的知识统一程度很高.
结论:
- NLM 展示了一种复杂的知识整合水平,反映了人类的自我.
- 这些发现支持了先进的人工智能中出现复杂认知结构的想法.
- 进一步的研究可以探索这种人工智能驱动的知识整合的性质和影响.
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