What to Predict? Exploring How Sentence Structure Influences Contrast Predictions in Humans and Large Language Models
Shuqi Wang, Xufeng Duan, Zhenguang Garry Cai · 2025
This study examines how sentence structure shapes contrast predictions in both humans and large language models (LLMs).Using Mandarin ditransitive constructions -double object (DO, "She gave the girl the candy, but not…") vs. prepositional object (PO, "She gave the candy to the girl, but not…") as a testbed, we employed a sentence continuation task involving three human groups (written, spoken, and prosodically normalized spoken stimuli) and three LLMs (GPT-4o, LLaMA-3, and Qwen-2.5).Two principal findings emerged: (1) Although human participants predominantly focused on the theme (e.g., "the candy"), contrast predictions were significantly modulated by sentence structure-particularly in spoken contexts, where the sentence-final element drew more attention.(2) While LLMs showed a similar reliance on structure, they displayed a larger effect size and more closely resembled human spoken data than written data, indicating a stronger emphasis on linear order in generating contrast predictions.By adopting a unified psycholinguistic paradigm, this study advances our understanding of predictive language processing for both humans and LLMs and informs research on humanmodel alignment in linguistic tasks.