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Updated: Jun 13, 2025

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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
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大型语言模型预测了人类在六种模式的感官判断.
Raja Marjieh1, Ilia Sucholutsky2, Pol van Rijn3
1Department of Psychology, Princeton University, Princeton, USA. raja.marjieh@princeton.edu.
Scientific reports
|September 13, 2024
概括
大型语言模型可以从文本中提取重要的感知信息,反映人类在颜色和声音等领域的判断. 即使没有直接视觉训练的模型也与人类感知有很强的相关性,突出了语言.
科学领域:
- 认知科学 认知科学
- 思想的哲学 思想的哲学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 认知科学和哲学中一个长期存在的挑战是理解多少感知信息只能从语言中获得.
- 研究语言表征和人类感知之间的关系对于理解认知至关重要.
研究的目的:
- 确定大型语言模型 (LLM) 可以从文本中恢复感知信息的程度.
- 用最先进的法学士来确定从语言中提取的感知信息的下限.
- 探索跨语言变异对LLMs内感知表征的影响.
主要方法:
- 在六个不同的心理物理数据集中,从GPT模型中引起对对相似性判断.
- 将LLM生成的判断与人类数据进行比较,以评估表示准确性.
- 将LLM应用于多语言颜色命名任务,以分析跨语言感知变化.
主要成果:
- 在所有测试领域中,LLM的判断显示了与人类数据的显著相关性,成功地恢复了已知的感知结构,如颜色轮和音调螺旋.
- 在视觉和语言方面进行联合训练的模型GPT-4没有显示出特定模式的优势,但在有或没有直接视觉输入的情况下产生了类似的结果.
- LLM复制了已知的色彩命名的跨语言变化 (例如,英语与俄语),证明了对语言感知相互作用的理解.
结论:
- 最先进的LLM可以从语言中提取大量的感知信息,作为认知科学研究的宝贵工具.
- 在LLM中,感知表现是强大的,与人类判断高度相关,即使仅依赖文本描述.
- 语言学士可以模拟特定语言对感知分类的影响,并提供对语言和感知之间的相互作用的见解.
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