AI能像人一样理解新造词吗？实验发现部分有效，但复合词仍难搞定。
Can AI mimic the human ability to define neologisms?
- 对比人类与ChatGPT对希腊语新词的释义，分三类考察。
- 对混成词和派生词，AI与人意见一致；复合词则完全不一致。
- 当以多数人类答案为标准时，AI在两类词上表现良好，适合研究语言认知。
语言学中一个持续争论的问题是人工智能（AI）能否在语言任务中有效模仿人类表现。尽管已有大量研究关注AI的语言能力，但对其如何定义通过不同构词方式形成的新生词（neologisms）却关注较少。本研究填补这一空白，考察了人类与AI在解释三类希腊语新词——混成词、复合词和派生词——时的一致性。研究采用在线实验，让人类参与者为新词选择最合适的定义，同时将相同提示输入ChatGPT。结果显示，对于混成词和派生词，人类与AI的回应存在合理一致性；但对于复合词，二者无一致意见。然而，若以人类多数意见为准，AI在混成词和派生词上的匹配度很高。这些发现凸显了人类语言的复杂性，也表明当前AI在理解复杂构词形式（尤其是复合词）方面仍面临挑战，亟需引入更先进的语义网络和上下文学习机制来提升其解释能力。
原文摘要 · Abstract (English)
One ongoing debate in linguistics is whether Artificial Intelligence (AI) can effectively mimic human performance in language-related tasks. While much research has focused on various linguistic abilities of AI, little attention has been given to how it defines neologisms formed through different word formation processes. This study addresses this gap by examining the degree of agreement between human and AI-generated responses in defining three types of Greek neologisms: blends, compounds, and derivatives. The study employed an online experiment in which human participants selected the most appropriate definitions for neologisms, while ChatGPT received identical prompts. The results revealed fair agreement between human and AI responses for blends and derivatives but no agreement for compounds. However, when considering the majority response among humans, agreement with AI was high for blends and derivatives. These findings highlight the complexity of human language and the challenges AI still faces in capturing its nuances. In particular, they suggest a need for integrating more advanced semantic networks and contextual learning mechanisms into AI models to improve their interpretation of complex word formations, especially compounds.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。