大型语言模型能产生有用的语言库吗? : 一个案例研究的单词频率效应在年轻的德国读者
Job Schepens1, Hanna Woloszyn2, Nicole Marx3
1Institute for Digital Humanities, University of Cologne, Cologne, Germany.
Open mind : discoveries in cognitive science
|October 31, 2025
概括
大型语言模型 (LLM) 可以为心理语言研究生成儿童故事体. 在LLM corpora中,即使词汇丰富度较低,也显示出对研究单词频率对阅读表现的影响有前途.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 语料库语言学 语料库语言学
背景情况:
- 语言体对于心理语言学研究至关重要.
- 现有的 corpora 可能无法完全捕捉当代语言使用或特定的人口统计.
- 大型语言模型 (LLM) 提供了一个新的方法来生成语料库.
研究的目的:
- 通过使用LLMs进行心理语言研究来产生新的语言体.
- 评估LLM生成的 corpora对估计单词频率对德国儿童阅读表现的影响的有用性.
- 将LLM生成的 corpora与现有的corpus (childLex) 进行比较.
主要方法:
- 促使三个不同的LLM创建基于书籍标题的儿童故事集.
- 模仿一个现有的语料库 (childLex).
- 执行具有反应时间的词汇决策任务,以评估单词频率效应.
主要成果:
- 单词频率在LLM corpora和childLex之间有很强的相关性.
- LLM corpora表现出较低的词汇丰富性,但解释了阅读表现的更多差异.
- 词汇丰富度较低的corpora通常更适合反应时间.
- 提示儿童定向的文本改进适合儿童阅读时间.
结论:
- 通过LLM生成的 corpora是心理语言学研究的可行资源,特别是用于研究单词频率效应.
- LLM corpora的词汇丰富度较低并不妨碍它们解释阅读性能差异的能力.
- 仔细提示和考虑LLM特征对于有效的语料库生成很重要.
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