@inproceedings{askari-etal-2025-babylms,
    title = "Are {B}aby{LM}s Deaf to {G}ricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models",
    author = {Askari, Raha  and
      Zarrie{\ss}, Sina  and
      Alacam, {\"O}zge  and
      Sieker, Judith},
    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.4/",
    pages = "52--65",
    ISBN = "TODO",
    abstract = "Implicit meanings are integral to human communication, making it essential for language models to be capable of identifying and interpreting them. Grice (1975) proposed a set of conversational maxims that guide cooperative dialogue, noting that speakers may deliberately violate these principles to express meanings beyond literal words, and that listeners, in turn, recognize such violations to draw pragmatic inferences.Building on Surian et al. (1996){'}s study of children{'}s sensitivity to violations of Gricean maxims, we introduce a novel benchmark to test whether language models pretrained on {\ensuremath{<}}10M and {\ensuremath{<}}100M tokens can distinguish maxim-adhering from maxim-violating utterances. We compare these BabyLMs across five maxims and situate their performance relative to children and a Large Language Model (LLM) pretrained on 3T tokens.We find that overall, models trained on {\ensuremath{<}}100M tokens outperform those trained on {\ensuremath{<}}10M, yet fall short of child-level and LLM competence. Our results suggest that modest data increases improve some aspects of pragmatic behavior, leading to finer-grained differentiation between pragmatic dimensions."
}