大型语言模型中的词汇函数的上下文组合
Christopher T Kello1, Polyphony Bruna2, Kanly Thao2
1University of California, Merced, 5200 N. Lake Rd, Merced, CA, 95076, USA. ckello@ucmerced.edu.
Behavior research methods
|December 9, 2025
概括
大型语言模型 (LLM) 与人类心理语言评级有很高的相关性,为心理词汇提供了新的见解. 然而,由于有限的训练数据模式,LLM目前很难模拟单词命名延迟.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 神经网络模型对心理语言学研究的词汇处理至关重要.
- 大型语言模型 (LLM) 提供了一种新的计算方法来研究心理词汇.
研究的目的:
- 评估LLMs产生词语心理语言评级的能力.
- 在不同的实验环境中,将LLM产生的评分与人类的判断进行比较.
- 评估LLM在预测单词命名延迟方面的表现.
主要方法:
- 在三个实验中使用了四个LLM来生成单词评分.
- 对LLM提示应用了上下文操纵来评估校准效应.
- 通过LLM生成的词命名延迟与人类数据进行了比较.
主要成果:
- 士学位评级与人类评级有很高的相关性,受评级模两可的影响.
- 在LLM提示的上下文变化改善了与人类数据的校准和相关性.
- 伦理理学研究院 (LLM) 的评价者之间的变化反映了人类的评价者之间的变化.
- 与人类相比,LLM在生成单词命名延迟方面表现出功能偏差.
结论:
- 在LLM中,利用上下文来组装通用词汇函数,而不是检索存储的数据.
- 目前的LLM词汇函数组合受到训练数据中共发生模式的限制.
- 未来的LLM开发需要更细致的时间模式来建模在线词汇过程.
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