@inproceedings{kukreja-etal-2026-better,
    title = "Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size",
    author = "Kukreja, Dikshant  and
      Sah, Kshitij  and
      Gupta, Gautam  and
      Anand, Avinash  and
      Shah, Rajiv Ratn  and
      Wang, Zhengkui  and
      Ng, Aik Beng  and
      Cambria, Erik",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1509/",
    pages = "30194--30209",
    ISBN = "979-8-89176-395-1",
    abstract = "Larger language models become simultaneously better and worse at handling contextual information{---}better at ignoring false claims, worse at ignoring irrelevant tokens. We formalize this apparent paradox through the first scaling laws for contextual entrainment, the tendency of models to favor tokens that appeared in context regardless of relevance. Analyzing the Cerebras-GPT (111M{--}13B) and Pythia (14M{--}12B) model families, we find entrainment follows predictable power-law scaling, but with opposite trends depending on context type: semantic contexts show decreasing entrainment with scale, while non-semantic contexts show increasing entrainment. Concretely, the largest models are four times more resistant to counterfactual misinformation than the smallest, yet simultaneously twice as prone to copying arbitrary tokens. These diverging trends, which replicate across model families, suggest that semantic filtering and mechanical copying are functionally distinct behaviors that scale in opposition. These opposing trends suggest that scaling alone does not resolve context sensitivity{---}it reshapes it."
}