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Scope interpretation in natural and artificial language processing.

Shaohua Fang1, Jacky He2, Xinmiao Liu3

  • 1Department of English, Purdue University, USA.

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Summary
This summary is machine-generated.

Quantifier scope processing in English and Chinese shows inverse scope is harder. Working memory affects offline interpretation, and large language models partially model human sentence processing.

Keywords:
Cross-linguisticLanguage modelsReal-time processingScope interpretationWorking memory

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Area of Science:

  • Psycholinguistics
  • Computational Linguistics
  • Cognitive Science

Background:

  • Understanding quantifier scope is crucial for sentence interpretation.
  • Cross-linguistic differences in processing quantified sentences remain an active research area.
  • Large language models (LLMs) offer new avenues for modeling human language processing.

Purpose of the Study:

  • To compare processing costs of inverse vs. surface scope in English and Chinese.
  • To investigate the role of working memory (WM) in quantifier scope interpretation.
  • To evaluate LLM alignment with human sentence processing patterns.

Main Methods:

  • Self-paced reading and comprehension questions for human participants.
  • Comparison with surprisal-based measures from autoregressive LLMs (GPT-2, BLOOM, Qwen2.5).

Main Results:

  • Inverse scope interpretations incurred greater processing costs and were less accessible.
  • Chinese showed a stronger preference for surface scope than English.
  • Working memory modulated offline interpretation but not real-time processing.
  • LLMs captured global scope interpretation patterns, with Qwen2.5-0.5B showing strongest correspondence with English human data.

Conclusions:

  • Cross-linguistic asymmetries exist in scope accessibility.
  • Real-time processing and offline interpretation involve distinct cognitive demands.
  • LLMs show potential but have limitations in modeling human sentence processing.