@inproceedings{salhan-etal-2025-teacher,
    title = "Teacher Demonstrations in a {B}aby{LM}{'}s Zone of Proximal Development for Contingent Multi-Turn Interaction",
    author = "Salhan, Suchir  and
      Gu, Hongyi  and
      Rooein, Donya  and
      Galvan-Sosa, Diana  and
      Gaudeau, Gabrielle  and
      Caines, Andrew  and
      Yuan, Zheng  and
      Buttery, Paula",
    editor = "Charpentier, Lucas  and
      Choshen, Leshem  and
      Cotterell, Ryan  and
      Gul, Mustafa Omer  and
      Hu, Michael Y.  and
      Liu, Jing  and
      Jumelet, Jaap  and
      Linzen, Tal  and
      Mueller, Aaron  and
      Ross, Candace  and
      Shah, Raj Sanjay  and
      Warstadt, Alex  and
      Wilcox, Ethan Gotlieb  and
      Williams, Adina",
    booktitle = "Proceedings of the First BabyLM Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.babylm-main.25/",
    pages = "323--355",
    ISBN = "TODO",
    abstract = "Multi-turn dialogues between a child and caregiver are characterized by a property called contingency {--} prompt, direct, and meaningful exchanges between interlocutors. We introduce ContingentChat, a Teacher{--}Student framework that benchmarks and improves multi-turn contingency in a BabyLM trained on 100M words. Using a novel alignment dataset for post-training, BabyLM generates responses that are more grammatical and cohesive. Experiments with adaptive Teacher decoding strategies show limited additional gains. ContingentChat highlights the positive benefits of targeted post-training on dialogue quality and presents contingency as a challenging goal for BabyLMs."
}