在自然和人工语言处理中的范围解释
Shaohua Fang1, Jacky He2, Xinmiao Liu3
1Department of English, Purdue University, USA.
Acta psychologica
|February 26, 2026
概括
英语和中文的量化器范围处理显示反向范围更难. 工作记忆影响离线解释,大型语言模型部分模拟人类的句子处理.
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:
- 了解量化器范围对于句子解释至关重要.
- 处理量化句子的跨语言差异仍然是一个活跃的研究领域.
- 大型语言模型 (LLM) 为模拟人类语言处理提供了新的途径.
研究的目的:
- 为了比较反向与表面范围的处理成本,用英语和中文.
- 调查工作记忆 (WM) 在量化器范围解释中的作用.
- 为了评估LLM与人类句子处理模式的对齐.
主要方法:
- 对人类参与者进行自律的阅读和理解问题.
- 与自动回归的LLM (GPT-2,BLOOM,Qwen2.5) 的基于surprisal的测量结果进行比较.
主要成果:
- 反向范围解释造成了更高的处理成本,并且更难以获得.
- 中国人对表面范围的偏好比英语更强.
- 工作内存调节离线解释,但不是实时处理.
- LLM捕获了全球范围的解释模式,其中Qwen2.5-0.5B与英语人体数据相对应最强.
结论:
- 在范围可访问性方面存在跨语言的不对称性.
- 实时处理和离线解释涉及不同的认知需求.
- 在模拟人类的句子处理方面,LLM显示出潜力,但存在局限性.
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