English(EN) Using LMs to Model the Effects of Context and Coreference during Sentence Comprehension

研究发现：大型语言模型的上下文窗口大小影响句子理解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员通过考察上下文窗口大小和指代消解对大型语言模型（LLMs）如GPT-2模拟人类句子理解的影响，进行了相关探索。研究结果表明存在一种U形关系，其中模拟工作记忆的受限上下文和长达1000个标记的扩展上下文都显示出高度的心理语言学契合度。通过代词化实体来破坏长距离指代关系，会显著降低较大上下文窗口的预测能力，这表明跟踪这些关系对于使大型语言模型的意外度与人类阅读行为保持一致至关重要。 AI

影响为理解大型语言模型如何处理语言提供了见解，可能为未来开发更具人类般理解能力的模型提供信息。

排序理由在arXiv上发表的学术论文，详细介绍了对大型语言模型行为的研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Kohei Kajikawa, Lin Ai, Tatsuki Kuribayashi, Ethan Gotlieb Wilcox · 2026-10-09 04:00

使用大型语言模型模拟上下文和指代消解对句子理解的影响

arXiv:2609.32119v2 Announce Type: replace Abstract: Language models (LMs) are often used as a tool to model human language processing. Recent studies suggest that severely restricting LMs' context window improves their fit to human psycholinguistic data by simulating human workin…

本文如何被排名

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使用大型语言模型模拟上下文和指代消解对句子理解的影响

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