@inproceedings{wang-etal-2025-predict,
    title = "What to Predict? Exploring How Sentence Structure Influences Contrast Predictions in Humans and Large Language Models",
    author = "Wang, Shuqi  and
      Duan, Xufeng  and
      Cai, Zhenguang",
    editor = "Kuribayashi, Tatsuki  and
      Rambelli, Giulia  and
      Takmaz, Ece  and
      Wicke, Philipp  and
      Li, Jixing  and
      Oh, Byung-Doh",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.cmcl-1.28/",
    pages = "244--252",
    ISBN = "979-8-89176-227-5",
    abstract = "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 human{--}model alignment in linguistic tasks."
}