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大型语言模型对儿童记忆和暗示能力的知识:评估先前实验结果的模型预测

Pekka Santtila¹, Yongjie Sun², Kristjan Kask³

¹New York University Shanghai, Shanghai, China; Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, New York University Shanghai, Shanghai, China.

Acta psychologica

|July 10, 2025

概括

大型语言模型 (LLM) 在法医环境中预测儿童的记忆和暗示性方面表现有前途. 这些先进的人工智能工具比人类专业人员取得了更高的准确性,有助于儿童性虐待调查.

科学领域:

法医心理学法医心理学
人工智能的人工智能
儿童发展儿童发展

背景情况:

对于专业人士来说,在法医环境中预测儿童的记忆和暗示能力,例如儿童性虐待 (CSA) 调查,是很困难的.
大型语言模型 (LLM) 提供先进的自然语言处理能力,在医学决策和调查中具有潜力.

研究的目的:

评估四个LLM的准确性 (GPT-4,克劳德3.5索内特,拉玛-3-405b,Erniebot) 在预测儿童的记忆和暗示性方面.
在这些法医预测任务中,将LLM的表现与人类专业人员进行比较.

主要方法:

四种最先进的LLM在受控环境中进行了测试.
来自八项研究的30个问题被用来产生4800个预测.
模型的准确性是通过基平方测试来评估整体和特异性能的.

主要成果:

在此过程中,LLM获得了65%的准确率,超过了人类专业人员的57%的准确率.
克劳德3.5索内特,GPT-4和拉玛-3-405b的表现优于埃尔尼博特.
在预测虚假记忆植入方面观察到特定的优势.

结论:

通过准确的行为预测,LLM证明了支持儿童性虐待 (CSA) 访谈的潜力.
进一步的研究是必要的,以验证在现实世界的法医设置LLM应用.

关键词:

行为模式预测行为模式预测.儿童性虐待儿童性虐待大型语言模型.

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