词汇知识是否能解释世界知识对代词解释的影响?

IF 1.1 3区 心理学 0 LANGUAGE & LINGUISTICS
Cameron R. Jones, Benjamin Bergen
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引用次数: 0

摘要

统计语言知识能在多大程度上解释世界知识对语言理解的影响?我们通过关注语言理解的一个核心方面:代词解析来解决这个问题。现有研究表明,理解者使用世界知识来解决代词问题,而分布假说及其在大型语言模型(LLMs)中的可操作性为纯语言信息如何驱动明显的世界知识效应提供了另一种解释。我们在两个实验中解决了这些问题。在实验 1 中,我们发现世界知识的可信度(通过规范化研究进行测量)对理解问题(探究代词解释)的回答有很强的影响。在实验 2 中,被试在阅读与世界知识相一致的代词解释相矛盾的连词时速度较慢,这意味着理解者会自发地运用世界知识。在控制了 GPT-3 的预测(一种 LLM)后,这两种效应仍然存在,这表明代词的解释至少部分是由世界知识而不是单词知识驱动的。我们根据验证和期望驱动的话语过程,提出了知识驱动代词解释的两种潜在机制。结果表明,虽然分布信息可能捕捉到了世界知识的某些方面,但人类理解者很可能会利用 LLM 无法获得的其他来源。
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Does word knowledge account for the effect of world knowledge on pronoun interpretation?
To what extent can statistical language knowledge account for the effects of world knowledge in language comprehension? We address this question by focusing on a core aspect of language understanding: pronoun resolution. While existing studies suggest that comprehenders use world knowledge to resolve pronouns, the distributional hypothesis and its operationalization in large language models (LLMs) provide an alternative account of how purely linguistic information could drive apparent world knowledge effects. We addressed these confounds in two experiments. In Experiment 1, we found a strong effect of world knowledge plausibility (measured using a norming study) on responses to comprehension questions that probed pronoun interpretation. In experiment 2, participants were slower to read continuations that contradicted world knowledge-consistent interpretations of a pronoun, implying that comprehenders deploy world knowledge spontaneously. Both effects persisted when controlling for the predictions of GPT-3, an LLM, suggesting that pronoun interpretation is at least partly driven by knowledge about the world and not the word. We propose two potential mechanisms by which knowledge-driven pronoun resolution occurs, based on validation- and expectation-driven discourse processes. The results suggest that while distributional information may capture some aspects of world knowledge, human comprehenders likely draw on other sources unavailable to LLMs.
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CiteScore
4.20
自引率
0.00%
发文量
34
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