利用语境来进行感知预测,使用词嵌入.
Georgia-Ann Carter1, Frank Keller1, Paul Hoffman2
1Institute for Language, Cognition and Computation, School of Informatics, The University of Edinburgh.
Cognitive science
|June 7, 2025
概括
来自语言模型的词嵌入显示出预测概念形状的良好性能,但对亮度的结果不佳. 语境化词语嵌入并没有显著改善这些感知预测.
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:
- 来自大型语言体的词嵌入用于表示语言含义.
- 关于这些嵌入是否捕捉概念的感知质量存在争论,影响嵌入语义理论.
- 仅仅从语言中推断感知属性可能表明语言辅助概念知识的获取.
研究的目的:
- 调查基于变压器的语言模型是否使用上下文化的词嵌入来改善对感知概念质量的预测.
- 评估词嵌入的预测人类对概念亮度和形状的评分的能力.
- 检查上下文对名词和形容词-名词短语嵌入的感知预测的影响.
主要方法:
- 产生了对大量概念的脱上下文化和上下文化Word2Vec和BERT嵌入.
- 将这些嵌入式的预测性能与人类对概念亮度和形状的评级进行了比较.
- 进行了两个专注于名词表示和形容词-名词短语表示的实验.
主要成果:
- 从词嵌入中,可以高准确地预测形状属性.
- 明亮性质的预测准确度较为适度.
- 将上下文添加到词嵌入中显示,对预测名词和形容词名词短语的感知特征的影响有限.
结论:
- 文字嵌入有效地编码形状信息,但对亮度不那么有效.
- 语言模型中的上下文化对从词嵌入中预测感知品质的影响有限.
- 这些发现有助于理解人类概念处理和语言模型可解释性体现的范围.
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