发表机构
Florida State University（佛罗里达州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究测试人类与大型语言模型能否从含焦点小品词的句子构建稳定标量表征，发现二者相似输出或源于不同底层机制。
AI 中文摘要
“even”和“only”这类焦点小品词是形式语义理论的核心，这类理论会对替代项集合构建结构化表征。“even”突出意外或极端的替代项，“only”则强调排他性。若这类标量表征具有鲁棒性和可推广性，应在不同语境和系统中产生一致判断。本研究测试人类与大型语言模型（LLMs）是否能从包含这些小品词的句子中构建稳定的标量表征。使用约100个项目的数据集，要求参与者和模型做出标量判断。初步结果显示，人类与LLMs的相似输出可能源于不同的底层机制。
英文摘要
Focus particles such as "even" and "only" are central to formal semantic theories that posit structured representations over sets of alternatives. "Even" highlights unexpected or extreme alternatives, while "only" enforces exclusivity. If such scalar representations are robust and generalizable, they should give rise to consistent judgments across contexts and systems. In this work, we test whether humans and large language models (LLMs) construct stable scalar representations from sentences containing these particles. Using a dataset of approximately 100 items, participants and models were asked to make scalar judgments. Preliminary results suggest that similar outputs across humans and LLMs may arise from different underlying mechanisms.
Comments3 pages, 1 figure, presented at 9th annual Conference on Cognitive Computational Neuroscience
