人类和机器的句子处理:大型语言模型作为一种更好地理解人类阅读的工具
Nikki G Kaye1, Peter C Gordon2
1Department of Psychology & Neuroscience, The University of North Carolina at Chapel Hill, CB#3270, Chapel Hill, NC, 26599-3270, USA.
Psychonomic bulletin & review
|August 13, 2025
概括
大型语言模型 (LLM) 可以使用下一个词的概率来估计文字处理负载. 这篇评论探讨了LLM衍生的和惊喜如何预测人类阅读理解指标.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 了解人类的增量语言处理是阅读研究的关键.
- 量化文字上下文对处理的影响一直是具有挑战性的.
- 人工智能的进步导致了能够生成类似人类文本的复杂大型语言模型 (LLM).
研究的目的:
- 审查使用LLM来测量文字处理负载的经验研究结果.
- 评估LLM衍生的指标是否预测人类阅读理解.
- 评估这种方法的方法和理论影响.
主要方法:
- 使用LLM来根据语言背景估计下一个词的概率.
- 从这些概率计算信息理论指标,如和惊喜值.
- 分析这些指标对在线人类阅读措施 (例如眼睛追踪,ERP) 的预测能力.
主要成果:
- 语言学士可以模拟语言中单词之间的概率关系.
- 来自LLM的和惊喜值作为处理负载的定量衡量.
- 这些指标显示了预测人类阅读理解数据的差异的潜力.
结论:
- 来自LLM的指标为研究增量语言处理提供了一种新的方法.
- 这种方法为阅读的认知机制提供了洞察力.
- 需要进一步的研究来完善和验证这些在认知科学中的计算措施.
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